






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 							UpperCAmelCase_     (		__lowerCamelCase		):
     __lowerCamelCase										=     (LMSDiscreteScheduler,)
     __lowerCamelCase										=     10




     def      __UpperCAmelCase     (							self  ,	**_lowerCAmelCase		):
          UpperCAmelCase__			:     Tuple										= {
              """num_train_timesteps""": 1100,
              """beta_start""": 0.0_0_0_1,
              """beta_end""": 0.0_2,
              """beta_schedule""": """linear""",
          }

          config.update(**_lowerCAmelCase		)
          return config




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




     def      __UpperCAmelCase     (							self		):
          for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  ,	[0.0_0_0_2, 0.0_0_2, 0.0_2]		):
               self.check_over_configs(beta_start=_lowerCAmelCase  ,	beta_end=_lowerCAmelCase		)




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




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




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




     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     Optional[Any]										= self.scheduler_classes[0]
          UpperCAmelCase__			:     Dict										= self.get_scheduler_config()
          UpperCAmelCase__			:     Optional[int]										= scheduler_class(**_lowerCAmelCase		)

          scheduler.set_timesteps(self.num_inference_steps		)

          UpperCAmelCase__			:     Tuple										= self.dummy_model()
          UpperCAmelCase__			:     str										= self.dummy_sample_deter * scheduler.init_noise_sigma

          for i, t in enumerate(scheduler.timesteps		):
               UpperCAmelCase__			:     Union[str, Any]										= scheduler.scale_model_input(_lowerCAmelCase  ,	_lowerCAmelCase		)

               UpperCAmelCase__			:     Optional[Any]										= model(_lowerCAmelCase  ,	_lowerCAmelCase		)

               UpperCAmelCase__			:     Union[str, Any]										= scheduler.step(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
               UpperCAmelCase__			:     str										= output.prev_sample

          UpperCAmelCase__			:     List[Any]										= torch.sum(torch.abs(_lowerCAmelCase		)		)
          UpperCAmelCase__			:     Any										= torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_sum.item() - 1_0_0_6.3_8_8		) < 1e-2
          assert abs(result_mean.item() - 1.3_1		) < 1e-3




     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     Union[str, Any]										= self.scheduler_classes[0]
          UpperCAmelCase__			:     Optional[Any]										= self.get_scheduler_config(prediction_type="""v_prediction"""		)
          UpperCAmelCase__			:     Tuple										= scheduler_class(**_lowerCAmelCase		)

          scheduler.set_timesteps(self.num_inference_steps		)

          UpperCAmelCase__			:     List[str]										= self.dummy_model()
          UpperCAmelCase__			:     int										= self.dummy_sample_deter * scheduler.init_noise_sigma

          for i, t in enumerate(scheduler.timesteps		):
               UpperCAmelCase__			:     Optional[int]										= scheduler.scale_model_input(_lowerCAmelCase  ,	_lowerCAmelCase		)

               UpperCAmelCase__			:     List[str]										= model(_lowerCAmelCase  ,	_lowerCAmelCase		)

               UpperCAmelCase__			:     Tuple										= scheduler.step(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
               UpperCAmelCase__			:     Any										= output.prev_sample

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

          assert abs(result_sum.item() - 0.0_0_1_7		) < 1e-2
          assert abs(result_mean.item() - 2.2676e-06		) < 1e-3




     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     int										= self.scheduler_classes[0]
          UpperCAmelCase__			:     Union[str, Any]										= self.get_scheduler_config()
          UpperCAmelCase__			:     Any										= scheduler_class(**_lowerCAmelCase		)

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

          UpperCAmelCase__			:     Any										= self.dummy_model()
          UpperCAmelCase__			:     Optional[int]										= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          UpperCAmelCase__			:     List[str]										= sample.to(_lowerCAmelCase		)

          for i, t in enumerate(scheduler.timesteps		):
               UpperCAmelCase__			:     Optional[Any]										= scheduler.scale_model_input(_lowerCAmelCase  ,	_lowerCAmelCase		)

               UpperCAmelCase__			:     Optional[Any]										= model(_lowerCAmelCase  ,	_lowerCAmelCase		)

               UpperCAmelCase__			:     Optional[Any]										= scheduler.step(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
               UpperCAmelCase__			:     Dict										= output.prev_sample

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

          assert abs(result_sum.item() - 1_0_0_6.3_8_8		) < 1e-2
          assert abs(result_mean.item() - 1.3_1		) < 1e-3




     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     Union[str, Any]										= self.scheduler_classes[0]
          UpperCAmelCase__			:     Dict										= self.get_scheduler_config()
          UpperCAmelCase__			:     Dict										= scheduler_class(**_lowerCAmelCase  ,	use_karras_sigmas=_lowerCAmelCase		)

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

          UpperCAmelCase__			:     str										= self.dummy_model()
          UpperCAmelCase__			:     Optional[int]										= self.dummy_sample_deter.to(_lowerCAmelCase		) * scheduler.init_noise_sigma
          UpperCAmelCase__			:     Optional[int]										= sample.to(_lowerCAmelCase		)

          for t in scheduler.timesteps:
               UpperCAmelCase__			:     List[Any]										= scheduler.scale_model_input(_lowerCAmelCase  ,	_lowerCAmelCase		)

               UpperCAmelCase__			:     str										= model(_lowerCAmelCase  ,	_lowerCAmelCase		)

               UpperCAmelCase__			:     int										= scheduler.step(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
               UpperCAmelCase__			:     Any										= output.prev_sample

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

          assert abs(result_sum.item() - 3_8_1_2.9_9_2_7		) < 2e-2
          assert abs(result_mean.item() - 4.9_6_4_8		) < 1e-3














import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							logging.get_logger(__name__)







class 							UpperCAmelCase_     (		__lowerCamelCase		):




     def __init__(							self  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
          warnings.warn(
              """The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
              """ use ViltImageProcessor instead."""  ,	_lowerCAmelCase  ,	)
          super().__init__(*_lowerCAmelCase  ,	**_lowerCAmelCase		)






