




'''simple docstring'''



import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest







@require_torchsde
class 		lowercase_   (			lowercase__       ):


   """simple docstring"""

   lowerCamelCase_      =						(DPMSolverSDEScheduler,)
   lowerCamelCase_      =						10



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				**__lowerCamelCase :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				{
          """num_train_timesteps""": 1_1_0_0,
          """beta_start""": 0.0_0_0_1,
          """beta_end""": 0.0_2,
          """beta_schedule""": """linear""",
          """noise_sampler_seed""": 0,
      }

      config.update(**_UpperCamelCase   )
      return config



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
         self.check_over_configs(num_train_timesteps=_UpperCamelCase   )



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      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=_UpperCamelCase  ,				beta_end=_UpperCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

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



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

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



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.scheduler_classes[0]
      _SCREAMING_SNAKE_CASE	=				self.get_scheduler_config()
      _SCREAMING_SNAKE_CASE	=				scheduler_class(**_UpperCamelCase   )

      scheduler.set_timesteps(self.num_inference_steps   )

      _SCREAMING_SNAKE_CASE	=				self.dummy_model()
      _SCREAMING_SNAKE_CASE	=				self.dummy_sample_deter * scheduler.init_noise_sigma
      _SCREAMING_SNAKE_CASE	=				sample.to(_UpperCamelCase   )

      for i, t in enumerate(scheduler.timesteps   ):
         _SCREAMING_SNAKE_CASE	=				scheduler.scale_model_input(_UpperCamelCase  ,				_UpperCamelCase   )

         _SCREAMING_SNAKE_CASE	=				model(_UpperCamelCase  ,				_UpperCamelCase   )

         _SCREAMING_SNAKE_CASE	=				scheduler.step(_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase   )
         _SCREAMING_SNAKE_CASE	=				output.prev_sample

      _SCREAMING_SNAKE_CASE	=				torch.sum(torch.abs(_UpperCamelCase   )   )
      _SCREAMING_SNAKE_CASE	=				torch.mean(torch.abs(_UpperCamelCase   )   )

      if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5   ) < 1e-2
         assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7   ) < 1e-3
      elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6   ) < 1e-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2   ) < 1e-3
      else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2   ) < 1e-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6   ) < 1e-3



   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.scheduler_classes[0]
      _SCREAMING_SNAKE_CASE	=				self.get_scheduler_config(prediction_type="v_prediction"   )
      _SCREAMING_SNAKE_CASE	=				scheduler_class(**_UpperCamelCase   )

      scheduler.set_timesteps(self.num_inference_steps   )

      _SCREAMING_SNAKE_CASE	=				self.dummy_model()
      _SCREAMING_SNAKE_CASE	=				self.dummy_sample_deter * scheduler.init_noise_sigma
      _SCREAMING_SNAKE_CASE	=				sample.to(_UpperCamelCase   )

      for i, t in enumerate(scheduler.timesteps   ):
         _SCREAMING_SNAKE_CASE	=				scheduler.scale_model_input(_UpperCamelCase  ,				_UpperCamelCase   )

         _SCREAMING_SNAKE_CASE	=				model(_UpperCamelCase  ,				_UpperCamelCase   )

         _SCREAMING_SNAKE_CASE	=				scheduler.step(_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase   )
         _SCREAMING_SNAKE_CASE	=				output.prev_sample

      _SCREAMING_SNAKE_CASE	=				torch.sum(torch.abs(_UpperCamelCase   )   )
      _SCREAMING_SNAKE_CASE	=				torch.mean(torch.abs(_UpperCamelCase   )   )

      if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3   ) < 1e-2
         assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4   ) < 1e-3
      elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3   ) < 1e-2
         assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7   ) < 1e-3
      else:
         assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5   ) < 1e-2
         assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1   ) < 1e-3



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.scheduler_classes[0]
      _SCREAMING_SNAKE_CASE	=				self.get_scheduler_config()
      _SCREAMING_SNAKE_CASE	=				scheduler_class(**_UpperCamelCase   )

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

      _SCREAMING_SNAKE_CASE	=				self.dummy_model()
      _SCREAMING_SNAKE_CASE	=				self.dummy_sample_deter.to(_UpperCamelCase   ) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
         _SCREAMING_SNAKE_CASE	=				scheduler.scale_model_input(_UpperCamelCase  ,				_UpperCamelCase   )

         _SCREAMING_SNAKE_CASE	=				model(_UpperCamelCase  ,				_UpperCamelCase   )

         _SCREAMING_SNAKE_CASE	=				scheduler.step(_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase   )
         _SCREAMING_SNAKE_CASE	=				output.prev_sample

      _SCREAMING_SNAKE_CASE	=				torch.sum(torch.abs(_UpperCamelCase   )   )
      _SCREAMING_SNAKE_CASE	=				torch.mean(torch.abs(_UpperCamelCase   )   )

      if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8   ) < 1e-2
         assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5   ) < 1e-3
      elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2   ) < 1e-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1   ) < 1e-3
      else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2   ) < 1e-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6   ) < 1e-3



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.scheduler_classes[0]
      _SCREAMING_SNAKE_CASE	=				self.get_scheduler_config()
      _SCREAMING_SNAKE_CASE	=				scheduler_class(**_UpperCamelCase  ,				use_karras_sigmas=_UpperCamelCase   )

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

      _SCREAMING_SNAKE_CASE	=				self.dummy_model()
      _SCREAMING_SNAKE_CASE	=				self.dummy_sample_deter.to(_UpperCamelCase   ) * scheduler.init_noise_sigma
      _SCREAMING_SNAKE_CASE	=				sample.to(_UpperCamelCase   )

      for t in scheduler.timesteps:
         _SCREAMING_SNAKE_CASE	=				scheduler.scale_model_input(_UpperCamelCase  ,				_UpperCamelCase   )

         _SCREAMING_SNAKE_CASE	=				model(_UpperCamelCase  ,				_UpperCamelCase   )

         _SCREAMING_SNAKE_CASE	=				scheduler.step(_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase   )
         _SCREAMING_SNAKE_CASE	=				output.prev_sample

      _SCREAMING_SNAKE_CASE	=				torch.sum(torch.abs(_UpperCamelCase   )   )
      _SCREAMING_SNAKE_CASE	=				torch.mean(torch.abs(_UpperCamelCase   )   )

      if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8   ) < 1e-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1   ) < 1e-2
      elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5   ) < 1e-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1   ) < 1e-2
      else:
         assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2   ) < 1e-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1   ) < 1e-2







'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCamelCase_			=						{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						['BridgeTowerImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BridgeTowerForContrastiveLearning',
     'BridgeTowerForImageAndTextRetrieval',
     'BridgeTowerForMaskedLM',
     'BridgeTowerModel',
     'BridgeTowerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bridgetower import (
     BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BridgeTowerConfig,
     BridgeTowerTextConfig,
     BridgeTowerVisionConfig,
 )
 from .processing_bridgetower import BridgeTowerProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bridgetower import BridgeTowerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bridgetower import (
      BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BridgeTowerForContrastiveLearning,
      BridgeTowerForImageAndTextRetrieval,
      BridgeTowerForMaskedLM,
      BridgeTowerModel,
      BridgeTowerPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase_			=						_LazyModule(__name__, globals()['__file__'], _import_structure)

