import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      (DEISMultistepScheduler,)
    __a  =      (("num_inference_steps", 25),)
    def 						UpperCamelCase_				(  self , **lowerCAmelCase   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Dict=      {
         '''num_train_timesteps''': 1000,
         '''beta_start''': 0.0001,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
         '''solver_order''': 2,
     }

     config.update(**lowerCAmelCase   )
     return config
    def 						UpperCamelCase_				(  self , lowerCAmelCase=0 , **lowerCAmelCase   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   str=      dict(self.forward_default_kwargs   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      kwargs.pop('''num_inference_steps''' , lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   str=      self.dummy_sample
     SCREAMING_SNAKE_CASE__:   str=      0.1 * sample
     SCREAMING_SNAKE_CASE__:   List[str]=      [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
      SCREAMING_SNAKE_CASE__:   str=      self.get_scheduler_config(**lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      scheduler_class(**lowerCAmelCase   )
      scheduler.set_timesteps(lowerCAmelCase   )
      # copy over dummy past residuals
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      dummy_past_residuals[: scheduler.config.solver_order]

      with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(lowerCAmelCase   )
       SCREAMING_SNAKE_CASE__:   Optional[int]=      scheduler_class.from_pretrained(lowerCAmelCase   )
       new_scheduler.set_timesteps(lowerCAmelCase   )
       # copy over dummy past residuals
       SCREAMING_SNAKE_CASE__:   Tuple=      dummy_past_residuals[: new_scheduler.config.solver_order]

      SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   List[Any]=      sample, sample
      for t in range(lowerCAmelCase , time_step + scheduler.config.solver_order + 1   ):
       SCREAMING_SNAKE_CASE__:   Optional[int]=      scheduler.step(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase   ).prev_sample
       SCREAMING_SNAKE_CASE__:   str=      new_scheduler.step(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase   ).prev_sample

       assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"
    def 						UpperCamelCase_				(  self   )       ->					int:
     pass
    def 						UpperCamelCase_				(  self , lowerCAmelCase=0 , **lowerCAmelCase   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   str=      dict(self.forward_default_kwargs   )
     SCREAMING_SNAKE_CASE__:   List[str]=      kwargs.pop('''num_inference_steps''' , lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.dummy_sample
     SCREAMING_SNAKE_CASE__:   int=      0.1 * sample
     SCREAMING_SNAKE_CASE__:   int=      [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
      SCREAMING_SNAKE_CASE__:   Tuple=      self.get_scheduler_config()
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      scheduler_class(**lowerCAmelCase   )
      scheduler.set_timesteps(lowerCAmelCase   )

      # copy over dummy past residuals (must be after setting timesteps)
      SCREAMING_SNAKE_CASE__:   List[Any]=      dummy_past_residuals[: scheduler.config.solver_order]

      with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(lowerCAmelCase   )
       SCREAMING_SNAKE_CASE__:   List[Any]=      scheduler_class.from_pretrained(lowerCAmelCase   )
       # copy over dummy past residuals
       new_scheduler.set_timesteps(lowerCAmelCase   )

       # copy over dummy past residual (must be after setting timesteps)
       SCREAMING_SNAKE_CASE__:   Dict=      dummy_past_residuals[: new_scheduler.config.solver_order]

      SCREAMING_SNAKE_CASE__:   Tuple=      scheduler.step(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase   ).prev_sample
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      new_scheduler.step(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase   ).prev_sample

      assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"
    def 						UpperCamelCase_				(  self , lowerCAmelCase=None , **lowerCAmelCase   )       ->					Any:
     if scheduler is None:
      SCREAMING_SNAKE_CASE__:   Dict=      self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.get_scheduler_config(**lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   int=      scheduler_class(**lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__:   str=      self.get_scheduler_config(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Any=      scheduler_class(**lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Dict=      10
     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.dummy_model()
     SCREAMING_SNAKE_CASE__:   Any=      self.dummy_sample_deter
     scheduler.set_timesteps(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
      SCREAMING_SNAKE_CASE__:   List[str]=      model(lowerCAmelCase , lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Any=      scheduler.step(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   ).prev_sample

     return sample
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      dict(self.forward_default_kwargs   )

     SCREAMING_SNAKE_CASE__:   Dict=      kwargs.pop('''num_inference_steps''' , lowerCAmelCase   )

     for scheduler_class in self.scheduler_classes:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.get_scheduler_config()
      SCREAMING_SNAKE_CASE__:   List[Any]=      scheduler_class(**lowerCAmelCase   )

      SCREAMING_SNAKE_CASE__:   str=      self.dummy_sample
      SCREAMING_SNAKE_CASE__:   List[str]=      0.1 * sample

      if num_inference_steps is not None and hasattr(lowerCAmelCase , '''set_timesteps'''   ):
       scheduler.set_timesteps(lowerCAmelCase   )
      elif num_inference_steps is not None and not hasattr(lowerCAmelCase , '''set_timesteps'''   ):
       SCREAMING_SNAKE_CASE__:   List[str]=      num_inference_steps

      # copy over dummy past residuals (must be done after set_timesteps)
      SCREAMING_SNAKE_CASE__:   Tuple=      [residual + 0.2, residual + 0.15, residual + 0.10]
      SCREAMING_SNAKE_CASE__:   List[str]=      dummy_past_residuals[: scheduler.config.solver_order]

      SCREAMING_SNAKE_CASE__:   Dict=      scheduler.timesteps[5]
      SCREAMING_SNAKE_CASE__:   List[str]=      scheduler.timesteps[6]

      SCREAMING_SNAKE_CASE__:   List[str]=      scheduler.step(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase   ).prev_sample
      SCREAMING_SNAKE_CASE__:   Optional[int]=      scheduler.step(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase   ).prev_sample

      self.assertEqual(output_a.shape , sample.shape   )
      self.assertEqual(output_a.shape , output_a.shape   )
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     SCREAMING_SNAKE_CASE__:   str=      DEISMultistepScheduler(**self.get_scheduler_config()   )
     SCREAMING_SNAKE_CASE__:   List[str]=      self.full_loop(scheduler=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Tuple=      torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_mean.item() - 0.23916   ) < 1e-3

     SCREAMING_SNAKE_CASE__:   int=      DPMSolverSinglestepScheduler.from_config(scheduler.config   )
     SCREAMING_SNAKE_CASE__:   int=      DPMSolverMultistepScheduler.from_config(scheduler.config   )
     SCREAMING_SNAKE_CASE__:   str=      UniPCMultistepScheduler.from_config(scheduler.config   )
     SCREAMING_SNAKE_CASE__:   str=      DEISMultistepScheduler.from_config(scheduler.config   )

     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.full_loop(scheduler=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Tuple=      torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_mean.item() - 0.23916   ) < 1e-3
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     for timesteps in [25, 50, 100, 999, 1000]:
      self.check_over_configs(num_train_timesteps=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     self.check_over_configs(thresholding=lowerCAmelCase   )
     for order in [1, 2, 3]:
      for solver_type in ["logrho"]:
       for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "sample"]:
         self.check_over_configs(
             thresholding=lowerCAmelCase , prediction_type=lowerCAmelCase , sample_max_value=lowerCAmelCase , algorithm_type='''deis''' , solver_order=lowerCAmelCase , solver_type=lowerCAmelCase , )
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     for prediction_type in ["epsilon", "v_prediction"]:
      self.check_over_configs(prediction_type=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     for algorithm_type in ["deis"]:
      for solver_type in ["logrho"]:
       for order in [1, 2, 3]:
        for prediction_type in ["epsilon", "sample"]:
         self.check_over_configs(
             solver_order=lowerCAmelCase , solver_type=lowerCAmelCase , prediction_type=lowerCAmelCase , algorithm_type=lowerCAmelCase , )
         SCREAMING_SNAKE_CASE__:   List[Any]=      self.full_loop(
             solver_order=lowerCAmelCase , solver_type=lowerCAmelCase , prediction_type=lowerCAmelCase , algorithm_type=lowerCAmelCase , )
         assert not torch.isnan(lowerCAmelCase   ).any(), "Samples have nan numbers"
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     self.check_over_configs(lower_order_final=lowerCAmelCase   )
     self.check_over_configs(lower_order_final=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
      self.check_over_forward(num_inference_steps=lowerCAmelCase , time_step=0   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.full_loop()
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_mean.item() - 0.23916   ) < 1e-3
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.full_loop(prediction_type='''v_prediction'''   )
     SCREAMING_SNAKE_CASE__:   str=      torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_mean.item() - 0.091   ) < 1e-3



    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__:   Tuple=      self.get_scheduler_config(thresholding=lowerCAmelCase , dynamic_thresholding_ratio=0   )
     SCREAMING_SNAKE_CASE__:   str=      scheduler_class(**lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      10
     SCREAMING_SNAKE_CASE__:   str=      self.dummy_model()
     SCREAMING_SNAKE_CASE__:   List[str]=      self.dummy_sample_deter.half()
     scheduler.set_timesteps(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
      SCREAMING_SNAKE_CASE__:   int=      model(lowerCAmelCase , lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Dict=      scheduler.step(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   ).prev_sample

     assert sample.dtype == torch.floataa


from typing import TYPE_CHECKING

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


lowercase_     :   List[str] 				=				{
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   Optional[Any] 				=				['MaskFormerFeatureExtractor']
      lowercase_     :   Dict 				=				['MaskFormerImageProcessor']


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   str 				=				[
          'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MaskFormerForInstanceSegmentation',
          'MaskFormerModel',
          'MaskFormerPreTrainedModel',
      ]
      lowercase_     :   Optional[Any] 				=				[
          'MaskFormerSwinBackbone',
          'MaskFormerSwinModel',
          'MaskFormerSwinPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
      from .configuration_maskformer_swin import MaskFormerSwinConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_maskformer import MaskFormerFeatureExtractor
            from .image_processing_maskformer import MaskFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_maskformer import (
                MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaskFormerForInstanceSegmentation,
                MaskFormerModel,
                MaskFormerPreTrainedModel,
            )
            from .modeling_maskformer_swin import (
                MaskFormerSwinBackbone,
                MaskFormerSwinModel,
                MaskFormerSwinPreTrainedModel,
            )


else:
      import sys

      lowercase_     :   Any 				=				_LazyModule(__name__, globals()['__file__'], _import_structure)

