





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class      A       (	__lowercase						):
      _snake_case							     =(DDPMScheduler,)



      def 	lowerCAmelCase__    (	self:						List[Any]		,  **_lowerCAmelCase:						List[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											={
             "num_train_timesteps": 1000,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "variance_type": "fixed_small",
             "clip_sample": True,
         }

         config.update(**_lowerCAmelCase       )
         return config



      def 	lowerCAmelCase__    (	self:						str       )						->    str:
         '''simple docstring'''

         for timesteps in [1, 5, 100, 1000]:
            self.check_over_configs(num_train_timesteps=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Optional[Any]:
         '''simple docstring'''

         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=_lowerCAmelCase		,  beta_end=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Any:
         '''simple docstring'''

         for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    List[str]:
         '''simple docstring'''

         for variance in ["fixed_small", "fixed_large", "other"]:
            self.check_over_configs(variance_type=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						List[str]       )						->    Optional[Any]:
         '''simple docstring'''

         for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Any:
         '''simple docstring'''

         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 	lowerCAmelCase__    (	self:						Tuple       )						->    List[str]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    int:
         '''simple docstring'''

         for t in [0, 500, 999]:
            self.check_over_forward(time_step=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=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       ) - 0.0_09_79       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.02       )       ) < 1e-5



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         UpperCAmelCase_											=len(_lowerCAmelCase       )

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter
         UpperCAmelCase_											=torch.manual_seed(0       )

         for t in reversed(range(_lowerCAmelCase       )       ):
            # 1. predict noise residual
            UpperCAmelCase_											=model(_lowerCAmelCase		,  _lowerCAmelCase       )

            # 2. predict previous mean of sample x_t-1
            UpperCAmelCase_											=scheduler.step(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  generator=_lowerCAmelCase       ).prev_sample

            # if t > 0:
            #     noise = self.dummy_sample_deter
            #     variance = scheduler.get_variance(t) ** (0.5) * noise
            #
            # sample = pred_prev_sample + variance
            UpperCAmelCase_											=pred_prev_sample

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 258.9606       ) < 1e-2
         assert abs(result_mean.item() - 0.33_72       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						Dict       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config(prediction_type="v_prediction"       )
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         UpperCAmelCase_											=len(_lowerCAmelCase       )

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter
         UpperCAmelCase_											=torch.manual_seed(0       )

         for t in reversed(range(_lowerCAmelCase       )       ):
            # 1. predict noise residual
            UpperCAmelCase_											=model(_lowerCAmelCase		,  _lowerCAmelCase       )

            # 2. predict previous mean of sample x_t-1
            UpperCAmelCase_											=scheduler.step(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  generator=_lowerCAmelCase       ).prev_sample

            # if t > 0:
            #     noise = self.dummy_sample_deter
            #     variance = scheduler.get_variance(t) ** (0.5) * noise
            #
            # sample = pred_prev_sample + variance
            UpperCAmelCase_											=pred_prev_sample

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 202.0296       ) < 1e-2
         assert abs(result_mean.item() - 0.26_31       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						List[str]       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         UpperCAmelCase_											=[100, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=_lowerCAmelCase       )

         UpperCAmelCase_											=scheduler.timesteps

         for i, timestep in enumerate(_lowerCAmelCase       ):
            if i == len(_lowerCAmelCase       ) - 1:
               UpperCAmelCase_											=-1
            else:
               UpperCAmelCase_											=timesteps[i + 1]

            UpperCAmelCase_											=scheduler.previous_timestep(_lowerCAmelCase       )
            UpperCAmelCase_											=prev_t.item()

            self.assertEqual(_lowerCAmelCase		,  _lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Dict       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         UpperCAmelCase_											=[100, 87, 50, 51, 0]

         with self.assertRaises(_lowerCAmelCase		,  msg="`custom_timesteps` must be in descending order."       ):
            scheduler.set_timesteps(timesteps=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         UpperCAmelCase_											=[100, 87, 50, 1, 0]
         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						List[str]       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         UpperCAmelCase_											=[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       )











import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__lowercase  :   Optional[int]						      ="""python tqdm regex requests packaging filelock numpy tokenizers""".split()
if sys.version_info < (3, 7):
     pkgs_to_check_at_runtime.append("""dataclasses""")
if sys.version_info < (3, 8):
     pkgs_to_check_at_runtime.append("""importlib_metadata""")

for pkg in pkgs_to_check_at_runtime:
     if pkg in deps:
          if pkg == "tokenizers":
               # must be loaded here, or else tqdm check may fail
               from .utils import is_tokenizers_available

               if not is_tokenizers_available():
                    continue  # not required, check version only if installed

          require_version_core(deps[pkg])
     else:
          raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")




def     a__ (					lowercase__  ,    lowercase__=None  ):






   '''simple docstring'''






   require_version(deps[pkg]  ,    lowercase__  )




