





import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 				_UpperCamelCase       (						_a  ):
       _UpperCamelCase					:       Optional[Any]       = (LMSDiscreteScheduler,)
       _UpperCamelCase					:       int       = 1_0

       def lowercase			(		self:			Dict       , **_SCREAMING_SNAKE_CASE:			Dict		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	{
                  "num_train_timesteps": 1100,
                  "beta_start": 0.00_01,
                  "beta_end": 0.02,
                  "beta_schedule": "linear",
              }

              config.update(**_UpperCAmelCase		)
              return config

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




              """simple docstring"""


              for timesteps in [10, 50, 100, 1000]:
                     self.check_over_configs(num_train_timesteps=_UpperCAmelCase		)

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




              """simple docstring"""


              for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]       , [0.00_02, 0.0_02, 0.02]		):
                     self.check_over_configs(beta_start=_UpperCAmelCase       , beta_end=_UpperCAmelCase		)

       def lowercase			(		self:			int		)							->			Optional[int]:




              """simple docstring"""


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

       def lowercase			(		self:			Any		)							->			List[str]:




              """simple docstring"""


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

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




              """simple docstring"""


              for t in [0, 500, 800]:
                     self.check_over_forward(time_step=_UpperCAmelCase		)

       def lowercase			(		self:			int		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.scheduler_classes[0]
              UpperCamelCase_ 							=	self.get_scheduler_config()
              UpperCamelCase_ 							=	scheduler_class(**_UpperCAmelCase		)

              scheduler.set_timesteps(self.num_inference_steps		)

              UpperCamelCase_ 							=	self.dummy_model()
              UpperCamelCase_ 							=	self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps		):
                     UpperCamelCase_ 							=	scheduler.scale_model_input(_UpperCAmelCase       , _UpperCAmelCase		)

                     UpperCamelCase_ 							=	model(_UpperCAmelCase       , _UpperCAmelCase		)

                     UpperCamelCase_ 							=	scheduler.step(_UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase		)
                     UpperCamelCase_ 							=	output.prev_sample

              UpperCamelCase_ 							=	torch.sum(torch.abs(_UpperCAmelCase		)		)
              UpperCamelCase_ 							=	torch.mean(torch.abs(_UpperCAmelCase		)		)

              assert abs(result_sum.item() - 1006.388		) < 1e-2
              assert abs(result_mean.item() - 1.31		) < 1e-3

       def lowercase			(		self:			str		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	self.scheduler_classes[0]
              UpperCamelCase_ 							=	self.get_scheduler_config(prediction_type="v_prediction"		)
              UpperCamelCase_ 							=	scheduler_class(**_UpperCAmelCase		)

              scheduler.set_timesteps(self.num_inference_steps		)

              UpperCamelCase_ 							=	self.dummy_model()
              UpperCamelCase_ 							=	self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps		):
                     UpperCamelCase_ 							=	scheduler.scale_model_input(_UpperCAmelCase       , _UpperCAmelCase		)

                     UpperCamelCase_ 							=	model(_UpperCAmelCase       , _UpperCAmelCase		)

                     UpperCamelCase_ 							=	scheduler.step(_UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase		)
                     UpperCamelCase_ 							=	output.prev_sample

              UpperCamelCase_ 							=	torch.sum(torch.abs(_UpperCAmelCase		)		)
              UpperCamelCase_ 							=	torch.mean(torch.abs(_UpperCAmelCase		)		)

              assert abs(result_sum.item() - 0.00_17		) < 1e-2
              assert abs(result_mean.item() - 2.2676e-06		) < 1e-3

       def lowercase			(		self:			Tuple		)							->			Any:




              """simple docstring"""


              UpperCamelCase_ 							=	self.scheduler_classes[0]
              UpperCamelCase_ 							=	self.get_scheduler_config()
              UpperCamelCase_ 							=	scheduler_class(**_UpperCAmelCase		)

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

              UpperCamelCase_ 							=	self.dummy_model()
              UpperCamelCase_ 							=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              UpperCamelCase_ 							=	sample.to(_UpperCAmelCase		)

              for i, t in enumerate(scheduler.timesteps		):
                     UpperCamelCase_ 							=	scheduler.scale_model_input(_UpperCAmelCase       , _UpperCAmelCase		)

                     UpperCamelCase_ 							=	model(_UpperCAmelCase       , _UpperCAmelCase		)

                     UpperCamelCase_ 							=	scheduler.step(_UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase		)
                     UpperCamelCase_ 							=	output.prev_sample

              UpperCamelCase_ 							=	torch.sum(torch.abs(_UpperCAmelCase		)		)
              UpperCamelCase_ 							=	torch.mean(torch.abs(_UpperCAmelCase		)		)

              assert abs(result_sum.item() - 1006.388		) < 1e-2
              assert abs(result_mean.item() - 1.31		) < 1e-3



       def lowercase			(		self:			Optional[int]		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	self.scheduler_classes[0]
              UpperCamelCase_ 							=	self.get_scheduler_config()
              UpperCamelCase_ 							=	scheduler_class(**_UpperCAmelCase       , use_karras_sigmas=_UpperCAmelCase		)

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

              UpperCamelCase_ 							=	self.dummy_model()
              UpperCamelCase_ 							=	self.dummy_sample_deter.to(_UpperCAmelCase		) * scheduler.init_noise_sigma
              UpperCamelCase_ 							=	sample.to(_UpperCAmelCase		)

              for t in scheduler.timesteps:
                     UpperCamelCase_ 							=	scheduler.scale_model_input(_UpperCAmelCase       , _UpperCAmelCase		)

                     UpperCamelCase_ 							=	model(_UpperCAmelCase       , _UpperCAmelCase		)

                     UpperCamelCase_ 							=	scheduler.step(_UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase		)
                     UpperCamelCase_ 							=	output.prev_sample

              UpperCamelCase_ 							=	torch.sum(torch.abs(_UpperCAmelCase		)		)
              UpperCamelCase_ 							=	torch.mean(torch.abs(_UpperCAmelCase		)		)

              assert abs(result_sum.item() - 3812.9927		) < 2e-2
              assert abs(result_mean.item() - 4.96_48		) < 1e-3


"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase_:       Optional[int]			     =       {"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Any			     =       [
     "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTHybridForImageClassification",
     "ViTHybridModel",
     "ViTHybridPreTrainedModel",
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Optional[Any]			     =       ["ViTHybridImageProcessor"]


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

 lowerCAmelCase_:       List[Any]			     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




