import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      (DDPMParallelScheduler,)
    def 						UpperCamelCase_				(  self , **lowerCAmelCase   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Dict=      {
         '''num_train_timesteps''': 1000,
         '''beta_start''': 0.0001,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
         '''variance_type''': '''fixed_small''',
         '''clip_sample''': True,
     }

     config.update(**lowerCAmelCase   )
     return config
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     for timesteps in [1, 5, 100, 1000]:
      self.check_over_configs(num_train_timesteps=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]   ):
      self.check_over_configs(beta_start=lowerCAmelCase , beta_end=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     for schedule in ["linear", "squaredcos_cap_v2"]:
      self.check_over_configs(beta_schedule=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     for variance in ["fixed_small", "fixed_large", "other"]:
      self.check_over_configs(variance_type=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					str:
     for clip_sample in [True, False]:
      self.check_over_configs(clip_sample=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     self.check_over_configs(thresholding=lowerCAmelCase   )
     for threshold in [0.5, 1.0, 2.0]:
      for prediction_type in ["epsilon", "sample", "v_prediction"]:
       self.check_over_configs(
           thresholding=lowerCAmelCase , prediction_type=lowerCAmelCase , sample_max_value=lowerCAmelCase , )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
      self.check_over_configs(prediction_type=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     for t in [0, 500, 999]:
      self.check_over_forward(time_step=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   Dict=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__:   Tuple=      scheduler_class(**lowerCAmelCase   )

     assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.00979   )   ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.02   )   ) < 1e-5
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   str=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__:   Tuple=      self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      scheduler_class(**lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Dict=      len(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Any=      self.dummy_model()
     SCREAMING_SNAKE_CASE__:   Tuple=      self.dummy_sample_deter
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.dummy_sample_deter + 0.1
     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.dummy_sample_deter - 0.1

     SCREAMING_SNAKE_CASE__:   Tuple=      samplea.shape[0]
     SCREAMING_SNAKE_CASE__:   Tuple=      torch.stack([samplea, samplea, samplea] , dim=0   )
     SCREAMING_SNAKE_CASE__:   Dict=      torch.arange(lowerCAmelCase   )[0:3, None].repeat(1 , lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Tuple=      model(samples.flatten(0 , 1   ) , timesteps.flatten(0 , 1   )   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      scheduler.batch_step_no_noise(lowerCAmelCase , timesteps.flatten(0 , 1   ) , samples.flatten(0 , 1   )   )

     SCREAMING_SNAKE_CASE__:   List[str]=      torch.sum(torch.abs(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   Any=      torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 1153.1833   ) < 1e-2
     assert abs(result_mean.item() - 0.5005   ) < 1e-3
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__:   str=      self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      scheduler_class(**lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[str]=      len(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      self.dummy_model()
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.dummy_sample_deter
     SCREAMING_SNAKE_CASE__:   Tuple=      torch.manual_seed(0   )

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

      # 2. predict previous mean of sample x_t-1
      SCREAMING_SNAKE_CASE__:   Optional[int]=      scheduler.step(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , generator=lowerCAmelCase   ).prev_sample

      SCREAMING_SNAKE_CASE__:   Tuple=      pred_prev_sample

     SCREAMING_SNAKE_CASE__:   Optional[int]=      torch.sum(torch.abs(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   Any=      torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 258.9606   ) < 1e-2
     assert abs(result_mean.item() - 0.3372   ) < 1e-3
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   str=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__:   Any=      self.get_scheduler_config(prediction_type='''v_prediction'''   )
     SCREAMING_SNAKE_CASE__:   Dict=      scheduler_class(**lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[str]=      len(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      self.dummy_model()
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.dummy_sample_deter
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torch.manual_seed(0   )

     for t in reversed(range(lowerCAmelCase   )   ):
      # 1. predict noise residual
      SCREAMING_SNAKE_CASE__:   int=      model(lowerCAmelCase , lowerCAmelCase   )

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

      SCREAMING_SNAKE_CASE__:   Any=      pred_prev_sample

     SCREAMING_SNAKE_CASE__:   str=      torch.sum(torch.abs(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   Tuple=      torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 202.0296   ) < 1e-2
     assert abs(result_mean.item() - 0.2631   ) < 1e-3
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__:   Any=      self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__:   List[str]=      scheduler_class(**lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   int=      [100, 87, 50, 1, 0]

     scheduler.set_timesteps(timesteps=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      scheduler.timesteps

     for i, timestep in enumerate(lowerCAmelCase   ):
      if i == len(lowerCAmelCase   ) - 1:
       SCREAMING_SNAKE_CASE__:   Optional[int]=      -1
      else:
       SCREAMING_SNAKE_CASE__:   str=      timesteps[i + 1]

      SCREAMING_SNAKE_CASE__:   int=      scheduler.previous_timestep(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   List[Any]=      prev_t.item()

      self.assertEqual(lowerCAmelCase , lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   Tuple=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      scheduler_class(**lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      [100, 87, 50, 51, 0]

     with self.assertRaises(lowerCAmelCase , msg='''`custom_timesteps` must be in descending order.'''   ):
      scheduler.set_timesteps(timesteps=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   str=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__:   Any=      self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      scheduler_class(**lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      [100, 87, 50, 1, 0]
     SCREAMING_SNAKE_CASE__:   Dict=      len(lowerCAmelCase   )

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



    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__:   List[str]=      self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__:   int=      scheduler_class(**lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      [scheduler.config.num_train_timesteps]

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


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_     :   Dict 				=				{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   str 				=				[
          'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'AutoformerForPrediction',
          'AutoformerModel',
          'AutoformerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_autoformer import (
          AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AutoformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_autoformer import (
                AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                AutoformerForPrediction,
                AutoformerModel,
                AutoformerPreTrainedModel,
            )

else:
      import sys

      lowercase_     :   Tuple 				=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

