






"""simple docstring"""




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       str			=					(DEISMultistepScheduler,)
 UpperCamelCase					:       Dict			=					(('''num_inference_steps''', 25),)


 def _lowercase	(     self		:						Dict							,						**UpperCAmelCase__		:						Tuple							)					->       str:
   _a  :       Optional[Any]             =	{
       """num_train_timesteps""": 1000,
       """beta_start""": 0.0_0_0_1,
       """beta_end""": 0.0_2,
       """beta_schedule""": """linear""",
       """solver_order""": 2,
   }

   config.update(**UpperCAmelCase__							)
   return config


 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						Optional[Any]=0							,						**UpperCAmelCase__		:						Tuple							)					->       Tuple:
   _a  :       List[Any]             =	dict(self.forward_default_kwargs							)
   _a  :       Union[str, Any]             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)
   _a  :       Union[str, Any]             =	self.dummy_sample
   _a  :       List[str]             =	0.1 * sample
   _a  :       Optional[int]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

   for scheduler_class in self.scheduler_classes:
     _a  :       Optional[Any]             =	self.get_scheduler_config(**UpperCAmelCase__							)
     _a  :       Tuple             =	scheduler_class(**UpperCAmelCase__							)
     scheduler.set_timesteps(UpperCAmelCase__							)
     # copy over dummy past residuals
     _a  :       int             =	dummy_past_residuals[: scheduler.config.solver_order]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(UpperCAmelCase__							)
       _a  :       Union[str, Any]             =	scheduler_class.from_pretrained(UpperCAmelCase__							)
       new_scheduler.set_timesteps(UpperCAmelCase__							)
       # copy over dummy past residuals
       _a  :       Tuple             =	dummy_past_residuals[: new_scheduler.config.solver_order]

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

       assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"


 def _lowercase	(     self		:						List[str]							)					->       Tuple:
   pass


 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						Tuple=0							,						**UpperCAmelCase__		:						List[Any]							)					->       Tuple:
   _a  :       str             =	dict(self.forward_default_kwargs							)
   _a  :       Optional[int]             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)
   _a  :       Optional[int]             =	self.dummy_sample
   _a  :       Dict             =	0.1 * sample
   _a  :       int             =	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

   for scheduler_class in self.scheduler_classes:
     _a  :       Any             =	self.get_scheduler_config()
     _a  :       Optional[Any]             =	scheduler_class(**UpperCAmelCase__							)
     scheduler.set_timesteps(UpperCAmelCase__							)

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

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(UpperCAmelCase__							)
       _a  :       Tuple             =	scheduler_class.from_pretrained(UpperCAmelCase__							)
       # copy over dummy past residuals
       new_scheduler.set_timesteps(UpperCAmelCase__							)

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

     _a  :       List[Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Tuple             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"


 def _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						List[str]=None							,						**UpperCAmelCase__		:						str							)					->       Optional[int]:
   if scheduler is None:
     _a  :       Dict             =	self.scheduler_classes[0]
     _a  :       Union[str, Any]             =	self.get_scheduler_config(**UpperCAmelCase__							)
     _a  :       Dict             =	scheduler_class(**UpperCAmelCase__							)

   _a  :       Union[str, Any]             =	self.scheduler_classes[0]
   _a  :       List[str]             =	self.get_scheduler_config(**UpperCAmelCase__							)
   _a  :       int             =	scheduler_class(**UpperCAmelCase__							)

   _a  :       str             =	10
   _a  :       Optional[Any]             =	self.dummy_model()
   _a  :       List[Any]             =	self.dummy_sample_deter
   scheduler.set_timesteps(UpperCAmelCase__							)

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

   return sample


 def _lowercase	(     self		:						List[str]							)					->       Any:
   _a  :       str             =	dict(self.forward_default_kwargs							)

   _a  :       Tuple             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)

   for scheduler_class in self.scheduler_classes:
     _a  :       Optional[int]             =	self.get_scheduler_config()
     _a  :       Union[str, Any]             =	scheduler_class(**UpperCAmelCase__							)

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

     if num_inference_steps is not None and hasattr(UpperCAmelCase__							,						"""set_timesteps"""							):
       scheduler.set_timesteps(UpperCAmelCase__							)
     elif num_inference_steps is not None and not hasattr(UpperCAmelCase__							,						"""set_timesteps"""							):
       _a  :       int             =	num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     _a  :       Optional[int]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
     _a  :       Tuple             =	dummy_past_residuals[: scheduler.config.solver_order]

     _a  :       Dict             =	scheduler.timesteps[5]
     _a  :       str             =	scheduler.timesteps[6]

     _a  :       Optional[Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       int             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

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


 def _lowercase	(     self		:						int							)					->       int:
   # make sure that iterating over schedulers with same config names gives same results
   # for defaults
   _a  :       List[Any]             =	DEISMultistepScheduler(**self.get_scheduler_config()							)
   _a  :       Dict             =	self.full_loop(scheduler=UpperCAmelCase__							)
   _a  :       Tuple             =	torch.mean(torch.abs(UpperCAmelCase__							)							)

   assert abs(result_mean.item() - 0.2_3_9_1_6							) < 1E-3

   _a  :       Optional[int]             =	DPMSolverSinglestepScheduler.from_config(scheduler.config							)
   _a  :       Dict             =	DPMSolverMultistepScheduler.from_config(scheduler.config							)
   _a  :       Optional[Any]             =	UniPCMultistepScheduler.from_config(scheduler.config							)
   _a  :       List[str]             =	DEISMultistepScheduler.from_config(scheduler.config							)

   _a  :       List[Any]             =	self.full_loop(scheduler=UpperCAmelCase__							)
   _a  :       Tuple             =	torch.mean(torch.abs(UpperCAmelCase__							)							)

   assert abs(result_mean.item() - 0.2_3_9_1_6							) < 1E-3


 def _lowercase	(     self		:						Dict							)					->       int:
   for timesteps in [25, 50, 100, 999, 1000]:
     self.check_over_configs(num_train_timesteps=UpperCAmelCase__							)


 def _lowercase	(     self		:						Union[str, Any]							)					->       Union[str, Any]:
   self.check_over_configs(thresholding=UpperCAmelCase__							)
   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=UpperCAmelCase__							,						prediction_type=UpperCAmelCase__							,						sample_max_value=UpperCAmelCase__							,						algorithm_type="""deis"""							,						solver_order=UpperCAmelCase__							,						solver_type=UpperCAmelCase__							,						)


 def _lowercase	(     self		:						Dict							)					->       Optional[Any]:
   for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							)					->       Optional[int]:
   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=UpperCAmelCase__							,						solver_type=UpperCAmelCase__							,						prediction_type=UpperCAmelCase__							,						algorithm_type=UpperCAmelCase__							,						)
           _a  :       int             =	self.full_loop(
               solver_order=UpperCAmelCase__							,						solver_type=UpperCAmelCase__							,						prediction_type=UpperCAmelCase__							,						algorithm_type=UpperCAmelCase__							,						)
           assert not torch.isnan(UpperCAmelCase__							).any(), "Samples have nan numbers"


 def _lowercase	(     self		:						List[Any]							)					->       List[Any]:
   self.check_over_configs(lower_order_final=UpperCAmelCase__							)
   self.check_over_configs(lower_order_final=UpperCAmelCase__							)


 def _lowercase	(     self		:						Optional[int]							)					->       Union[str, Any]:
   for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
     self.check_over_forward(num_inference_steps=UpperCAmelCase__							,						time_step=0							)


 def _lowercase	(     self		:						int							)					->       int:
   _a  :       Union[str, Any]             =	self.full_loop()
   _a  :       Optional[Any]             =	torch.mean(torch.abs(UpperCAmelCase__							)							)

   assert abs(result_mean.item() - 0.2_3_9_1_6							) < 1E-3


 def _lowercase	(     self		:						int							)					->       Tuple:
   _a  :       List[Any]             =	self.full_loop(prediction_type="""v_prediction"""							)
   _a  :       Any             =	torch.mean(torch.abs(UpperCAmelCase__							)							)

   assert abs(result_mean.item() - 0.0_9_1							) < 1E-3







 def _lowercase	(     self		:						Optional[Any]							)					->       Optional[int]:
   _a  :       Tuple             =	self.scheduler_classes[0]
   _a  :       Any             =	self.get_scheduler_config(thresholding=UpperCAmelCase__							,						dynamic_thresholding_ratio=0							)
   _a  :       Tuple             =	scheduler_class(**UpperCAmelCase__							)

   _a  :       int             =	10
   _a  :       Dict             =	self.dummy_model()
   _a  :       List[Any]             =	self.dummy_sample_deter.half()
   scheduler.set_timesteps(UpperCAmelCase__							)

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

   assert sample.dtype == torch.floataa









"""simple docstring"""




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

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

