




"""simple docstring"""
import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest
class    a		(					A__    ):
  UpperCamelCase		:			Optional[Any]         =   (DDIMScheduler,)
  UpperCamelCase		:			Tuple         =   (('eta', 0.0), ('num_inference_steps', 5_0))



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            ={
         """num_train_timesteps""": 1000,
         """beta_start""": 0.0_0_0_1,
         """beta_end""": 0.0_2,
         """beta_schedule""": """linear""",
         """clip_sample""": True,
     }

     config.update(**lowerCAmelCase						)
     return config



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


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	str            =10, 0.0

     SCREAMING_SNAKE_CASE_:	List[Any]            =self.dummy_model()
     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.dummy_sample_deter

     scheduler.set_timesteps(lowerCAmelCase						)

     for t in scheduler.timesteps:
        SCREAMING_SNAKE_CASE_:	str            =model(lowerCAmelCase    ,						lowerCAmelCase						)
        SCREAMING_SNAKE_CASE_:	List[str]            =scheduler.step(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase						).prev_sample

     return sample



  def 		lowerCamelCase__    (  self	:   Dict						)       ->   Tuple:


     '''simple docstring'''




     for timesteps in [100, 500, 1000]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   str:


     '''simple docstring'''




     for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	Optional[Any]            =self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE_:	List[str]            =self.get_scheduler_config(steps_offset=1						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =scheduler_class(**lowerCAmelCase						)
     scheduler.set_timesteps(5						)
     assert torch.equal(scheduler.timesteps    ,						torch.LongTensor([801, 601, 401, 201, 1]						)						)



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


     '''simple docstring'''




     for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,						[0.0_0_2, 0.0_2, 0.2, 2]						):
        self.check_over_configs(beta_start=lowerCAmelCase    ,						beta_end=lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   List[Any]						)       ->   Optional[Any]:


     '''simple docstring'''




     for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=lowerCAmelCase						)



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


     '''simple docstring'''




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



  def 		lowerCamelCase__    (  self	:   List[Any]						)       ->   Dict:


     '''simple docstring'''




     for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   str						)       ->   str:


     '''simple docstring'''




     for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   Optional[int]						)       ->   Dict:


     '''simple docstring'''




     for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   Any						)       ->   int:


     '''simple docstring'''




     self.check_over_configs(thresholding=lowerCAmelCase						)
     for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(
               thresholding=lowerCAmelCase    ,						prediction_type=lowerCAmelCase    ,						sample_max_value=lowerCAmelCase    ,						)



  def 		lowerCamelCase__    (  self	:   str						)       ->   Tuple:


     '''simple docstring'''




     for t in [1, 10, 49]:
        self.check_over_forward(time_step=lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   Optional[Any]:


     '''simple docstring'''




     for t, num_inference_steps in zip([1, 10, 50]    ,						[10, 50, 500]						):
        self.check_over_forward(time_step=lowerCAmelCase    ,						num_inference_steps=lowerCAmelCase						)



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


     '''simple docstring'''




     for t, eta in zip([1, 10, 49]    ,						[0.0, 0.5, 1.0]						):
        self.check_over_forward(time_step=lowerCAmelCase    ,						eta=lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   int						)       ->   Optional[int]:


     '''simple docstring'''




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

     assert torch.sum(torch.abs(scheduler._get_variance(0    ,						0						) - 0.0						)						) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(420    ,						400						) - 0.1_4_7_7_1						)						) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(980    ,						960						) - 0.3_2_4_6_0						)						) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(0    ,						0						) - 0.0						)						) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487    ,						486						) - 0.0_0_9_7_9						)						) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999    ,						998						) - 0.0_2						)						) < 1E-5



  def 		lowerCamelCase__    (  self	:   str						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[Any]            =self.full_loop()

     SCREAMING_SNAKE_CASE_:	Any            =torch.sum(torch.abs(lowerCAmelCase						)						)
     SCREAMING_SNAKE_CASE_:	Any            =torch.mean(torch.abs(lowerCAmelCase						)						)

     assert abs(result_sum.item() - 1_7_2.0_0_6_7						) < 1E-2
     assert abs(result_mean.item() - 0.2_2_3_9_6_7						) < 1E-3



  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =self.full_loop(prediction_type="""v_prediction"""						)

     SCREAMING_SNAKE_CASE_:	List[str]            =torch.sum(torch.abs(lowerCAmelCase						)						)
     SCREAMING_SNAKE_CASE_:	Tuple            =torch.mean(torch.abs(lowerCAmelCase						)						)

     assert abs(result_sum.item() - 5_2.5_3_0_2						) < 1E-2
     assert abs(result_mean.item() - 0.0_6_8_4						) < 1E-3



  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.full_loop(set_alpha_to_one=lowerCAmelCase    ,						beta_start=0.0_1						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =torch.sum(torch.abs(lowerCAmelCase						)						)
     SCREAMING_SNAKE_CASE_:	int            =torch.mean(torch.abs(lowerCAmelCase						)						)

     assert abs(result_sum.item() - 1_4_9.8_2_9_5						) < 1E-2
     assert abs(result_mean.item() - 0.1_9_5_1						) < 1E-3





  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.full_loop(set_alpha_to_one=lowerCAmelCase    ,						beta_start=0.0_1						)
     SCREAMING_SNAKE_CASE_:	int            =torch.sum(torch.abs(lowerCAmelCase						)						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =torch.mean(torch.abs(lowerCAmelCase						)						)

     assert abs(result_sum.item() - 1_4_9.0_7_8_4						) < 1E-2
     assert abs(result_mean.item() - 0.1_9_4_1						) < 1E-3








"""simple docstring"""
import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


_UpperCAmelCase       =     logging.get_logger(__name__)
class    a		(					UpperCAmelCase__    ):
  UpperCamelCase		:			int         =   'hybrid-clip'
  UpperCamelCase		:			Optional[int]         =   True



  def __init__(  self	:   List[Any]    ,						lowerCAmelCase	:   Any=512    ,						**lowerCAmelCase	:   Optional[Any]						)       ->   List[str]:


     '''simple docstring'''




     super().__init__(**lowerCAmelCase						)

     if "text_config" not in kwargs:
        raise ValueError("""`text_config` can not be `None`."""						)

     if "vision_config" not in kwargs:
        raise ValueError("""`vision_config` can not be `None`."""						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =kwargs.pop("""text_config"""						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =kwargs.pop("""vision_config"""						)

     SCREAMING_SNAKE_CASE_:	int            =text_config.pop("""model_type"""						)
     SCREAMING_SNAKE_CASE_:	str            =vision_config.pop("""model_type"""						)

     from transformers import AutoConfig

     SCREAMING_SNAKE_CASE_:	Optional[int]            =AutoConfig.for_model(lowerCAmelCase    ,						**lowerCAmelCase						)

     if vision_model_type == "clip":
        SCREAMING_SNAKE_CASE_:	Tuple            =AutoConfig.for_model(lowerCAmelCase    ,						**lowerCAmelCase						).vision_config
     elif vision_model_type == "clip_vision_model":
        from transformers import CLIPVisionConfig

        SCREAMING_SNAKE_CASE_:	List[str]            =CLIPVisionConfig(**lowerCAmelCase						)
     else:
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =AutoConfig.for_model(lowerCAmelCase    ,						**lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	Any            =projection_dim
     SCREAMING_SNAKE_CASE_:	int            =1.0



  @classmethod
  def 		lowerCamelCase__    (  cls	:   List[Any]    ,						lowerCAmelCase	:   PretrainedConfig    ,						lowerCAmelCase	:   PretrainedConfig    ,						**lowerCAmelCase	:   str						)       ->   Optional[Any]:


     '''simple docstring'''




     return cls(text_config=text_config.to_dict()    ,						vision_config=vision_config.to_dict()    ,						**lowerCAmelCase						)





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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =copy.deepcopy(self.__dict__						)
     SCREAMING_SNAKE_CASE_:	List[str]            =self.text_config.to_dict()
     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.vision_config.to_dict()
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.__class__.model_type
     return output



