import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   (DPMSolverSinglestepScheduler,)
    __a						=   (("num_inference_steps", 25),)
    def     UpperCamelCase_					(	self			,			**lowerCAmelCase )							->       str:
     SCREAMING_SNAKE_CASE__: Any=   {
         '''num_train_timesteps''': 1000,
         '''beta_start''': 0.0001,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
         '''solver_order''': 2,
         '''prediction_type''': '''epsilon''',
         '''thresholding''': False,
         '''sample_max_value''': 1.0,
         '''algorithm_type''': '''dpmsolver++''',
         '''solver_type''': '''midpoint''',
         '''lambda_min_clipped''': -float('''inf''' ),
         '''variance_type''': None,
     }

     config.update(**lowerCAmelCase )
     return config
    def     UpperCamelCase_					(	self			,			lowerCAmelCase=0			,			**lowerCAmelCase )							->       Any:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   dict(self.forward_default_kwargs )
     SCREAMING_SNAKE_CASE__: Tuple=   kwargs.pop('''num_inference_steps'''			,			lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Tuple=   self.dummy_sample
     SCREAMING_SNAKE_CASE__: List[Any]=   0.1 * sample
     SCREAMING_SNAKE_CASE__: Optional[Any]=   [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__: Dict=   scheduler_class(**lowerCAmelCase )
      scheduler.set_timesteps(lowerCAmelCase )
      # copy over dummy past residuals
      SCREAMING_SNAKE_CASE__: Tuple=   dummy_past_residuals[: scheduler.config.solver_order]

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

      SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Dict=   sample, sample
      for t in range(lowerCAmelCase			,			time_step + scheduler.config.solver_order + 1 ):
       SCREAMING_SNAKE_CASE__: str=   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 )							->       Optional[Any]:
     pass
    def     UpperCamelCase_					(	self			,			lowerCAmelCase=0			,			**lowerCAmelCase )							->       Dict:
     SCREAMING_SNAKE_CASE__: Optional[int]=   dict(self.forward_default_kwargs )
     SCREAMING_SNAKE_CASE__: List[Any]=   kwargs.pop('''num_inference_steps'''			,			lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Tuple=   self.dummy_sample
     SCREAMING_SNAKE_CASE__: Dict=   0.1 * sample
     SCREAMING_SNAKE_CASE__: Optional[Any]=   [residual + 0.2, residual + 0.15, residual + 0.10]

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

      # copy over dummy past residuals (must be after setting timesteps)
      SCREAMING_SNAKE_CASE__: Tuple=   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__: Union[str, Any]=   dummy_past_residuals[: new_scheduler.config.solver_order]

      SCREAMING_SNAKE_CASE__: Dict=   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			,			lowerCAmelCase=None			,			**lowerCAmelCase )							->       List[Any]:
     if scheduler is None:
      SCREAMING_SNAKE_CASE__: Dict=   self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE__: Dict=   self.get_scheduler_config(**lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: List[str]=   scheduler_class(**lowerCAmelCase )

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

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

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

     return sample
    def     UpperCamelCase_					(	self )							->       List[str]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
     SCREAMING_SNAKE_CASE__: Tuple=   50
     SCREAMING_SNAKE_CASE__: List[Any]=   self.dummy_model()
     SCREAMING_SNAKE_CASE__: List[str]=   self.dummy_sample_deter
     scheduler.set_timesteps(lowerCAmelCase )

     # make sure that the first t is uneven
     for i, t in enumerate(scheduler.timesteps[3:] ):
      SCREAMING_SNAKE_CASE__: Dict=   model(lowerCAmelCase			,			lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: str=   scheduler.step(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase ).prev_sample

     SCREAMING_SNAKE_CASE__: Union[str, Any]=   torch.mean(torch.abs(lowerCAmelCase ) )

     assert abs(result_mean.item() - 0.2574 ) < 1e-3
    def     UpperCamelCase_					(	self )							->       str:
     for timesteps in [25, 50, 100, 999, 1000]:
      self.check_over_configs(num_train_timesteps=lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       int:
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     SCREAMING_SNAKE_CASE__: Optional[int]=   DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
     SCREAMING_SNAKE_CASE__: int=   self.full_loop(scheduler=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Optional[int]=   torch.mean(torch.abs(lowerCAmelCase ) )

     assert abs(result_mean.item() - 0.2791 ) < 1e-3

     SCREAMING_SNAKE_CASE__: Tuple=   DEISMultistepScheduler.from_config(scheduler.config )
     SCREAMING_SNAKE_CASE__: str=   DPMSolverMultistepScheduler.from_config(scheduler.config )
     SCREAMING_SNAKE_CASE__: Dict=   UniPCMultistepScheduler.from_config(scheduler.config )
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   DPMSolverSinglestepScheduler.from_config(scheduler.config )

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

     assert abs(result_mean.item() - 0.2791 ) < 1e-3
    def     UpperCamelCase_					(	self )							->       int:
     self.check_over_configs(thresholding=lowerCAmelCase )
     for order in [1, 2, 3]:
      for solver_type in ["midpoint", "heun"]:
       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='''dpmsolver++'''			,			solver_order=lowerCAmelCase			,			solver_type=lowerCAmelCase			,			)
    def     UpperCamelCase_					(	self )							->       Any:
     for prediction_type in ["epsilon", "v_prediction"]:
      self.check_over_configs(prediction_type=lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       int:
     for algorithm_type in ["dpmsolver", "dpmsolver++"]:
      for solver_type in ["midpoint", "heun"]:
       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__: int=   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 )							->       int:
     self.check_over_configs(lower_order_final=lowerCAmelCase )
     self.check_over_configs(lower_order_final=lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Dict:
     self.check_over_configs(lambda_min_clipped=-float('''inf''' ) )
     self.check_over_configs(lambda_min_clipped=-5.1 )
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     self.check_over_configs(variance_type=lowerCAmelCase )
     self.check_over_configs(variance_type='''learned_range''' )
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     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 )							->       List[str]:
     SCREAMING_SNAKE_CASE__: List[str]=   self.full_loop()
     SCREAMING_SNAKE_CASE__: List[Any]=   torch.mean(torch.abs(lowerCAmelCase ) )

     assert abs(result_mean.item() - 0.2791 ) < 1e-3
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Optional[int]=   self.full_loop(use_karras_sigmas=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Dict=   torch.mean(torch.abs(lowerCAmelCase ) )

     assert abs(result_mean.item() - 0.2248 ) < 1e-3
    def     UpperCamelCase_					(	self )							->       Dict:
     SCREAMING_SNAKE_CASE__: Any=   self.full_loop(prediction_type='''v_prediction''' )
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   torch.mean(torch.abs(lowerCAmelCase ) )

     assert abs(result_mean.item() - 0.1453 ) < 1e-3
    def     UpperCamelCase_					(	self )							->       Dict:
     SCREAMING_SNAKE_CASE__: str=   self.full_loop(prediction_type='''v_prediction'''			,			use_karras_sigmas=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Tuple=   torch.mean(torch.abs(lowerCAmelCase ) )

     assert abs(result_mean.item() - 0.0649 ) < 1e-3



    def     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: int=   self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__: int=   self.get_scheduler_config(thresholding=lowerCAmelCase			,			dynamic_thresholding_ratio=0 )
     SCREAMING_SNAKE_CASE__: Optional[int]=   scheduler_class(**lowerCAmelCase )

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

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

     assert sample.dtype == torch.floataa







"""simple docstring"""

from datetime import datetime

import matplotlib.pyplot as plt
import torch






def 		_snake_case	(   _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      for param in module.parameters():
            _A     =  False






def 		_snake_case	(   )       ->   Tuple:






      '''simple docstring'''


      _A     =  'cuda' if torch.cuda.is_available() else 'cpu'
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            _A     =  'mps'
      if device == "mps":
            print(
                'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                ' with generations.'				)
      return device






def 		_snake_case	(   _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  plt.imshow(_snake_case				)
      fig.axes.get_xaxis().set_visible(_snake_case				)
      fig.axes.get_yaxis().set_visible(_snake_case				)
      plt.show()







def 		_snake_case	(   )       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  datetime.now()
      _A     =  current_time.strftime('%H:%M:%S'				)
      return timestamp

