

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      List[str]										=(UniPCMultistepScheduler,)
  lowerCamelCase   :      Union[str, Any]										=(("num_inference_steps", 25),)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							**lowerCAmelCase			:   List[Any]   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						{
         """num_train_timesteps""": 10_00,
         """beta_start""": 0.0001,
         """beta_end""": 0.02,
         """beta_schedule""": """linear""",
         """solver_order""": 2,
         """solver_type""": """bh2""",
     }

     config.update(**lowerCAmelCase   )
     return config







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   Tuple=0	,							**lowerCAmelCase			:   List[str]   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						dict(self.forward_default_kwargs   )
     __lowerCAmelCase  :      str    =						kwargs.pop("""num_inference_steps"""	,							lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						self.dummy_sample
     __lowerCAmelCase  :      Any    =						0.1 * sample
     __lowerCAmelCase  :      str    =						[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

        __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[Any]    =						sample, sample
        for t in range(lowerCAmelCase	,							time_step + scheduler.config.solver_order + 1   ):
           __lowerCAmelCase  :      str    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							**lowerCAmelCase   ).prev_sample
           __lowerCAmelCase  :      Optional[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 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Optional[Any]=0	,							**lowerCAmelCase			:   Optional[int]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						dict(self.forward_default_kwargs   )
     __lowerCAmelCase  :      str    =						kwargs.pop("""num_inference_steps"""	,							lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						self.dummy_sample
     __lowerCAmelCase  :      List[str]    =						0.1 * sample
     __lowerCAmelCase  :      List[Any]    =						[residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
        __lowerCAmelCase  :      Dict    =						self.get_scheduler_config()
        __lowerCAmelCase  :      Dict    =						scheduler_class(**lowerCAmelCase   )
        scheduler.set_timesteps(lowerCAmelCase   )

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

        with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(lowerCAmelCase   )
           __lowerCAmelCase  :      List[str]    =						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)
           __lowerCAmelCase  :      List[Any]    =						dummy_past_residuals[: new_scheduler.config.solver_order]

        __lowerCAmelCase  :      str    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							**lowerCAmelCase   ).prev_sample
        __lowerCAmelCase  :      Optional[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 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   Any=None	,							**lowerCAmelCase			:   Any   )							->     Tuple:






     """simple docstring"""







     if scheduler is None:
        __lowerCAmelCase  :      Optional[Any]    =						self.scheduler_classes[0]
        __lowerCAmelCase  :      Optional[Any]    =						self.get_scheduler_config(**lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[Any]    =						scheduler_class(**lowerCAmelCase   )

     __lowerCAmelCase  :      List[str]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      List[Any]    =						self.get_scheduler_config(**lowerCAmelCase   )
     __lowerCAmelCase  :      Union[str, Any]    =						scheduler_class(**lowerCAmelCase   )

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

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

     return sample







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						dict(self.forward_default_kwargs   )

     __lowerCAmelCase  :      Any    =						kwargs.pop("""num_inference_steps"""	,							lowerCAmelCase   )

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

        __lowerCAmelCase  :      str    =						self.dummy_sample
        __lowerCAmelCase  :      Tuple    =						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"""   ):
           __lowerCAmelCase  :      Tuple    =						num_inference_steps

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

        __lowerCAmelCase  :      Optional[int]    =						scheduler.timesteps[5]
        __lowerCAmelCase  :      List[str]    =						scheduler.timesteps[6]

        __lowerCAmelCase  :      Optional[int]    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							**lowerCAmelCase   ).prev_sample
        __lowerCAmelCase  :      Union[str, Any]    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							**lowerCAmelCase   ).prev_sample

        self.assertEqual(output_a.shape	,							sample.shape   )
        self.assertEqual(output_a.shape	,							output_a.shape   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						UniPCMultistepScheduler(**self.get_scheduler_config()   )
     __lowerCAmelCase  :      Optional[int]    =						self.full_loop(scheduler=lowerCAmelCase   )
     __lowerCAmelCase  :      Any    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_mean.item() - 0.2464   ) < 1e-3

     __lowerCAmelCase  :      Tuple    =						DPMSolverSinglestepScheduler.from_config(scheduler.config   )
     __lowerCAmelCase  :      List[Any]    =						DEISMultistepScheduler.from_config(scheduler.config   )
     __lowerCAmelCase  :      Optional[int]    =						DPMSolverMultistepScheduler.from_config(scheduler.config   )
     __lowerCAmelCase  :      Tuple    =						UniPCMultistepScheduler.from_config(scheduler.config   )

     __lowerCAmelCase  :      Union[str, Any]    =						self.full_loop(scheduler=lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_mean.item() - 0.2464   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     int:






     """simple docstring"""







     for timesteps in [25, 50, 1_00, 9_99, 10_00]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase   )







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






     """simple docstring"""







     self.check_over_configs(thresholding=lowerCAmelCase   )
     for order in [1, 2, 3]:
        for solver_type in ["bh1", "bh2"]:
           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	,							solver_order=lowerCAmelCase	,							solver_type=lowerCAmelCase	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Any:






     """simple docstring"""







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







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






     """simple docstring"""







     for solver_type in ["bh1", "bh2"]:
        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	,							)
              __lowerCAmelCase  :      Optional[Any]    =						self.full_loop(
                  solver_order=lowerCAmelCase	,							solver_type=lowerCAmelCase	,							prediction_type=lowerCAmelCase	,							)
              assert not torch.isnan(lowerCAmelCase   ).any(), "Samples have nan numbers"







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Tuple:






     """simple docstring"""







     self.check_over_configs(lower_order_final=lowerCAmelCase   )
     self.check_over_configs(lower_order_final=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     int:






     """simple docstring"""







     for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
        self.check_over_forward(num_inference_steps=lowerCAmelCase	,							time_step=0   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.full_loop()
     __lowerCAmelCase  :      Dict    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_mean.item() - 0.2464   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.full_loop(prediction_type="""v_prediction"""   )
     __lowerCAmelCase  :      Tuple    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_mean.item() - 0.1014   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      Optional[Any]    =						self.get_scheduler_config(thresholding=lowerCAmelCase	,							dynamic_thresholding_ratio=0   )
     __lowerCAmelCase  :      Optional[int]    =						scheduler_class(**lowerCAmelCase   )

     __lowerCAmelCase  :      Union[str, Any]    =						10
     __lowerCAmelCase  :      Dict    =						self.dummy_model()
     __lowerCAmelCase  :      int    =						self.dummy_sample_deter.half()
     scheduler.set_timesteps(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
        __lowerCAmelCase  :      str    =						model(lowerCAmelCase	,							lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   ).prev_sample

     assert sample.dtype == torch.floataa







  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							**lowerCAmelCase			:   Union[str, Any]   )							->     Optional[int]:






     """simple docstring"""







     for scheduler_class in self.scheduler_classes:
        __lowerCAmelCase  :      int    =						self.get_scheduler_config(**lowerCAmelCase   )
        __lowerCAmelCase  :      Tuple    =						scheduler_class(**lowerCAmelCase   )

        scheduler.set_timesteps(scheduler.config.num_train_timesteps   )
        assert len(scheduler.timesteps.unique()   ) == scheduler.num_inference_steps





__UpperCAmelCase								=			"""
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

__UpperCAmelCase								=			[{"""type""": """code""", """content""": INSTALL_CONTENT}]
__UpperCAmelCase								=			{
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}


