




import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class    __lowerCAmelCase       (							lowerCAmelCase):
       _a 					=						(DDPMParallelScheduler,)






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple       ,					**_lowerCAmelCase:					Any				):
         lowercase    :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(**_lowerCAmelCase				)
         return config






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         for timesteps in [1, 5, 1_00, 10_00]:
           self.check_over_configs(num_train_timesteps=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         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 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=_lowerCAmelCase				)






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






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






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         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 					SCREAMING_SNAKE_CASE    (   self:					str				):
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
           self.check_over_configs(prediction_type=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         for t in [0, 5_00, 9_99]:
           self.check_over_forward(time_step=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         lowercase    :Union[str, Any]      	=						self.scheduler_classes[0]
         lowercase    :Union[str, Any]      	=						self.get_scheduler_config()
         lowercase    :Union[str, Any]      	=						scheduler_class(**_lowerCAmelCase				)

         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 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :str      	=						self.scheduler_classes[0]
         lowercase    :List[str]      	=						self.get_scheduler_config()
         lowercase    :List[Any]      	=						scheduler_class(**_lowerCAmelCase				)

         lowercase    :int      	=						len(_lowerCAmelCase				)

         lowercase    :Dict      	=						self.dummy_model()
         lowercase    :Union[str, Any]      	=						self.dummy_sample_deter
         lowercase    :Optional[int]      	=						self.dummy_sample_deter + 0.1
         lowercase    :Tuple      	=						self.dummy_sample_deter - 0.1

         lowercase    :Any      	=						samplea.shape[0]
         lowercase    :Dict      	=						torch.stack([samplea, samplea, samplea]       ,					dim=0				)
         lowercase    :Union[str, Any]      	=						torch.arange(_lowerCAmelCase				)[0:3, None].repeat(1       ,					_lowerCAmelCase				)

         lowercase    :List[str]      	=						model(samples.flatten(0       ,					1				)       ,					timesteps.flatten(0       ,					1				)				)
         lowercase    :List[Any]      	=						scheduler.batch_step_no_noise(_lowerCAmelCase       ,					timesteps.flatten(0       ,					1				)       ,					samples.flatten(0       ,					1				)				)

         lowercase    :str      	=						torch.sum(torch.abs(_lowerCAmelCase				)				)
         lowercase    :Dict      	=						torch.mean(torch.abs(_lowerCAmelCase				)				)

         assert abs(result_sum.item() - 11_53.18_33				) < 1e-2
         assert abs(result_mean.item() - 0.50_05				) < 1e-3






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :Dict      	=						self.scheduler_classes[0]
         lowercase    :List[Any]      	=						self.get_scheduler_config()
         lowercase    :str      	=						scheduler_class(**_lowerCAmelCase				)

         lowercase    :int      	=						len(_lowerCAmelCase				)

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

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

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

           lowercase    :Optional[int]      	=						pred_prev_sample

         lowercase    :Union[str, Any]      	=						torch.sum(torch.abs(_lowerCAmelCase				)				)
         lowercase    :Any      	=						torch.mean(torch.abs(_lowerCAmelCase				)				)

         assert abs(result_sum.item() - 2_58.96_06				) < 1e-2
         assert abs(result_mean.item() - 0.33_72				) < 1e-3






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         lowercase    :List[Any]      	=						self.scheduler_classes[0]
         lowercase    :List[str]      	=						self.get_scheduler_config(prediction_type="v_prediction"				)
         lowercase    :Union[str, Any]      	=						scheduler_class(**_lowerCAmelCase				)

         lowercase    :str      	=						len(_lowerCAmelCase				)

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

         for t in reversed(range(_lowerCAmelCase				)				):
           # 1. predict noise residual
           lowercase    :Union[str, Any]      	=						model(_lowerCAmelCase       ,					_lowerCAmelCase				)

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

           lowercase    :List[str]      	=						pred_prev_sample

         lowercase    :Any      	=						torch.sum(torch.abs(_lowerCAmelCase				)				)
         lowercase    :Optional[Any]      	=						torch.mean(torch.abs(_lowerCAmelCase				)				)

         assert abs(result_sum.item() - 2_02.02_96				) < 1e-2
         assert abs(result_mean.item() - 0.26_31				) < 1e-3






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :str      	=						self.scheduler_classes[0]
         lowercase    :Tuple      	=						self.get_scheduler_config()
         lowercase    :Tuple      	=						scheduler_class(**_lowerCAmelCase				)

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

         scheduler.set_timesteps(timesteps=_lowerCAmelCase				)

         lowercase    :str      	=						scheduler.timesteps

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

           lowercase    :Dict      	=						scheduler.previous_timestep(_lowerCAmelCase				)
           lowercase    :List[str]      	=						prev_t.item()

           self.assertEqual(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         lowercase    :Optional[Any]      	=						self.scheduler_classes[0]
         lowercase    :str      	=						self.get_scheduler_config()
         lowercase    :str      	=						scheduler_class(**_lowerCAmelCase				)

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

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






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         lowercase    :List[str]      	=						self.scheduler_classes[0]
         lowercase    :str      	=						self.get_scheduler_config()
         lowercase    :str      	=						scheduler_class(**_lowerCAmelCase				)

         lowercase    :Union[str, Any]      	=						[1_00, 87, 50, 1, 0]
         lowercase    :List[str]      	=						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 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         lowercase    :Optional[int]      	=						self.scheduler_classes[0]
         lowercase    :int      	=						self.get_scheduler_config()
         lowercase    :Optional[int]      	=						scheduler_class(**_lowerCAmelCase				)

         lowercase    :Tuple      	=						[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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase	:						List[Any]			=     {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						Union[str, Any]			=     [
         "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "Swinv2ForImageClassification",
         "Swinv2ForMaskedImageModeling",
         "Swinv2Model",
         "Swinv2PreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swinva import (
              SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwinvaForImageClassification,
              SwinvaForMaskedImageModeling,
              SwinvaModel,
              SwinvaPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase	:						Tuple			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



