





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class UpperCAmelCase_							(   _SCREAMING_SNAKE_CASE   ):
  __lowerCAmelCase      :	str  	=      (HeunDiscreteScheduler,)
  __lowerCAmelCase      :	List[str]  	=      10


  def 	__UpperCAmelCase    (       self   ,**__snake_case   ):



       """simple docstring"""





       A_					      =					{
           '''num_train_timesteps''': 1_1_0_0,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**__snake_case   )
       return config


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]   ,[0.0002, 0.002, 0.02]   ):
            self.check_over_configs(beta_start=__snake_case   ,beta_end=__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       for schedule in ["linear", "scaled_linear", "exp"]:
            self.check_over_configs(beta_schedule=__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       for clip_sample_range in [1.0, 2.0, 3.0]:
            self.check_over_configs(clip_sample_range=__snake_case   ,clip_sample=__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





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


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.scheduler_classes[0]
       A_					      =					self.get_scheduler_config()
       A_					      =					scheduler_class(**__snake_case   )

       scheduler.set_timesteps(self.num_inference_steps   )

       A_					      =					self.dummy_model()
       A_					      =					self.dummy_sample_deter * scheduler.init_noise_sigma
       A_					      =					sample.to(__snake_case   )

       for i, t in enumerate(scheduler.timesteps   ):
            A_					      =					scheduler.scale_model_input(__snake_case   ,__snake_case   )

            A_					      =					model(__snake_case   ,__snake_case   )

            A_					      =					scheduler.step(__snake_case   ,__snake_case   ,__snake_case   )
            A_					      =					output.prev_sample

       A_					      =					torch.sum(torch.abs(__snake_case   )   )
       A_					      =					torch.mean(torch.abs(__snake_case   )   )

       if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 0.1233   ) < 1E-2
            assert abs(result_mean.item() - 0.0002   ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 0.1233   ) < 1E-2
            assert abs(result_mean.item() - 0.0002   ) < 1E-3


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.scheduler_classes[0]
       A_					      =					self.get_scheduler_config(prediction_type='''v_prediction'''   )
       A_					      =					scheduler_class(**__snake_case   )

       scheduler.set_timesteps(self.num_inference_steps   )

       A_					      =					self.dummy_model()
       A_					      =					self.dummy_sample_deter * scheduler.init_noise_sigma
       A_					      =					sample.to(__snake_case   )

       for i, t in enumerate(scheduler.timesteps   ):
            A_					      =					scheduler.scale_model_input(__snake_case   ,__snake_case   )

            A_					      =					model(__snake_case   ,__snake_case   )

            A_					      =					scheduler.step(__snake_case   ,__snake_case   ,__snake_case   )
            A_					      =					output.prev_sample

       A_					      =					torch.sum(torch.abs(__snake_case   )   )
       A_					      =					torch.mean(torch.abs(__snake_case   )   )

       if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 4.69_34E-07   ) < 1E-2
            assert abs(result_mean.item() - 6.11_12E-10   ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07   ) < 1E-2
            assert abs(result_mean.item() - 0.0002   ) < 1E-3


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.scheduler_classes[0]
       A_					      =					self.get_scheduler_config()
       A_					      =					scheduler_class(**__snake_case   )

       scheduler.set_timesteps(self.num_inference_steps   ,device=__snake_case   )

       A_					      =					self.dummy_model()
       A_					      =					self.dummy_sample_deter.to(__snake_case   ) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
            A_					      =					scheduler.scale_model_input(__snake_case   ,__snake_case   )

            A_					      =					model(__snake_case   ,__snake_case   )

            A_					      =					scheduler.step(__snake_case   ,__snake_case   ,__snake_case   )
            A_					      =					output.prev_sample

       A_					      =					torch.sum(torch.abs(__snake_case   )   )
       A_					      =					torch.mean(torch.abs(__snake_case   )   )

       if str(__snake_case   ).startswith('''cpu'''   ):
            # The following sum varies between 148 and 156 on mps. Why?
            assert abs(result_sum.item() - 0.1233   ) < 1E-2
            assert abs(result_mean.item() - 0.0002   ) < 1E-3
       elif str(__snake_case   ).startswith('''mps'''   ):
            # Larger tolerance on mps
            assert abs(result_mean.item() - 0.0002   ) < 1E-2
       else:
            # CUDA
            assert abs(result_sum.item() - 0.1233   ) < 1E-2
            assert abs(result_mean.item() - 0.0002   ) < 1E-3




  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.scheduler_classes[0]
       A_					      =					self.get_scheduler_config()
       A_					      =					scheduler_class(**__snake_case   ,use_karras_sigmas=__snake_case   )

       scheduler.set_timesteps(self.num_inference_steps   ,device=__snake_case   )

       A_					      =					self.dummy_model()
       A_					      =					self.dummy_sample_deter.to(__snake_case   ) * scheduler.init_noise_sigma
       A_					      =					sample.to(__snake_case   )

       for t in scheduler.timesteps:
            A_					      =					scheduler.scale_model_input(__snake_case   ,__snake_case   )

            A_					      =					model(__snake_case   ,__snake_case   )

            A_					      =					scheduler.step(__snake_case   ,__snake_case   ,__snake_case   )
            A_					      =					output.prev_sample

       A_					      =					torch.sum(torch.abs(__snake_case   )   )
       A_					      =					torch.mean(torch.abs(__snake_case   )   )

       assert abs(result_sum.item() - 0.0_0015   ) < 1E-2
       assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95E-07   ) < 1E-2










from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
a__			:    List[str]			      =		load_iris()
iris.keys()


print(f'''Target names: \n {iris.target_names} ''')
print(f'''\n Features: \n {iris.feature_names}''')

# Train set e Test set
a__      ,							a__      ,							a__      ,							a__			:    Any			      =		train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

a__			:    List[Any]			      =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
a__			:    Optional[Any]			      =		[[1, 2, 1, 4], [2, 3, 4, 5]]

a__			:    Union[str, Any]			      =		knn.predict(X_new)

print(
    f'''\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'''
    f''' {iris['target_names'][prediction]}'''
)



