





'''simple docstring'''

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    (UniPCMultistepScheduler,)
 __lowercase       		=    (("""num_inference_steps""", 25),)
 def 					UpperCAmelCase_							(    self				:List[Any]						,	**lowercase_				:Optional[int]							)->							List[str]:
     A__    =      {
         "num_train_timesteps": 10_00,
         "beta_start": 0.0_0_0_1,
         "beta_end": 0.0_2,
         "beta_schedule": "linear",
         "solver_order": 2,
         "solver_type": "bh2",
     }

     config.update(**lowercase_							)
     return config
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:Union[str, Any]=0						,	**lowercase_				:Dict							)->							str:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config(**lowercase_							)
         A__    =      scheduler_class(**lowercase_							)
         scheduler.set_timesteps(lowercase_							)
         # copy over dummy past residuals
         A__    =      dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(lowercase_							)
             A__    =      scheduler_class.from_pretrained(lowercase_							)
             new_scheduler.set_timesteps(lowercase_							)
             # copy over dummy past residuals
             A__    =      dummy_past_residuals[: new_scheduler.config.solver_order]

         A__,     A__    =      sample, sample
         for t in range(lowercase_						,	time_step + scheduler.config.solver_order + 1							):
             A__    =      scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
             A__    =      new_scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

             assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Union[str, Any]=0						,	**lowercase_				:Any							)->							Optional[Any]:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)
         scheduler.set_timesteps(lowercase_							)

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

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

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

         A__    =      scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Any=None						,	**lowercase_				:Dict							)->							Dict:
     if scheduler is None:
         A__    =      self.scheduler_classes[0]
         A__    =      self.get_scheduler_config(**lowercase_							)
         A__    =      scheduler_class(**lowercase_							)

     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(**lowercase_							)
     A__    =      scheduler_class(**lowercase_							)

     A__    =      10
     A__    =      self.dummy_model()
     A__    =      self.dummy_sample_deter
     scheduler.set_timesteps(lowercase_							)

     for i, t in enumerate(scheduler.timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     return sample
 def 					UpperCAmelCase_							(    self				:int							)->							Any:
     A__    =      dict(self.forward_default_kwargs							)

     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         A__    =      self.dummy_sample
         A__    =      0.1 * sample

         if num_inference_steps is not None and hasattr(lowercase_						,	"set_timesteps"							):
             scheduler.set_timesteps(lowercase_							)
         elif num_inference_steps is not None and not hasattr(lowercase_						,	"set_timesteps"							):
             A__    =      num_inference_steps

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

         A__    =      scheduler.timesteps[5]
         A__    =      scheduler.timesteps[6]

         A__    =      scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         self.assertEqual(output_a.shape						,	sample.shape							)
         self.assertEqual(output_a.shape						,	output_a.shape							)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							str:
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     A__    =      UniPCMultistepScheduler(**self.get_scheduler_config()							)
     A__    =      self.full_loop(scheduler=lowercase_							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3

     A__    =      DPMSolverSinglestepScheduler.from_config(scheduler.config							)
     A__    =      DEISMultistepScheduler.from_config(scheduler.config							)
     A__    =      DPMSolverMultistepScheduler.from_config(scheduler.config							)
     A__    =      UniPCMultistepScheduler.from_config(scheduler.config							)

     A__    =      self.full_loop(scheduler=lowercase_							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Union[str, Any]:
     for timesteps in [25, 50, 1_00, 9_99, 10_00]:
         self.check_over_configs(num_train_timesteps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     self.check_over_configs(thresholding=lowercase_							)
     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=lowercase_						,	prediction_type=lowercase_						,	sample_max_value=lowercase_						,	solver_order=lowercase_						,	solver_type=lowercase_						,	)
 def 					UpperCAmelCase_							(    self				:str							)->							Dict:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_							)
 def 					UpperCAmelCase_							(    self				:str							)->							Union[str, Any]:
     for solver_type in ["bh1", "bh2"]:
         for order in [1, 2, 3]:
             for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=lowercase_						,	solver_type=lowercase_						,	prediction_type=lowercase_						,	)
                 A__    =      self.full_loop(
                     solver_order=lowercase_						,	solver_type=lowercase_						,	prediction_type=lowercase_						,	)
                 assert not torch.isnan(lowercase_							).any(), "Samples have nan numbers"
 def 					UpperCAmelCase_							(    self				:Dict							)->							Any:
     self.check_over_configs(lower_order_final=lowercase_							)
     self.check_over_configs(lower_order_final=lowercase_							)
 def 					UpperCAmelCase_							(    self				:str							)->							Union[str, Any]:
     for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
         self.check_over_forward(num_inference_steps=lowercase_						,	time_step=0							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							str:
     A__    =      self.full_loop()
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							str:
     A__    =      self.full_loop(prediction_type="v_prediction"							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_mean.item() - 0.1_0_1_4							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Tuple							)->							List[Any]:
     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(thresholding=lowercase_						,	dynamic_thresholding_ratio=0							)
     A__    =      scheduler_class(**lowercase_							)

     A__    =      10
     A__    =      self.dummy_model()
     A__    =      self.dummy_sample_deter.half()
     scheduler.set_timesteps(lowercase_							)

     for i, t in enumerate(scheduler.timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     assert sample.dtype == torch.floataa



 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	**lowercase_				:str							)->							Any:
     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config(**lowercase_							)
         A__    =      scheduler_class(**lowercase_							)

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










'''simple docstring'''

__lowerCAmelCase							:     Dict		     ="\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__lowerCAmelCase							:     Optional[Any]		     =[{"type": "code", "content": INSTALL_CONTENT}]
__lowerCAmelCase							:     Tuple		     ={
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}



