






'''simple docstring'''







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

# Load iris file
UpperCamelCase_					=				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
UpperCamelCase_,     UpperCamelCase_,     UpperCamelCase_,     UpperCamelCase_					=				train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

UpperCamelCase_					=				KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
UpperCamelCase_					=				[[1, 2, 1, 4], [2, 3, 4, 5]]

UpperCamelCase_					=				knn.predict(X_new)

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







'''simple docstring'''







import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    List[Any]				       =					(DDIMScheduler,)
      SCREAMING_SNAKE_CASE				:    str				       =					(("eta", 0.0), ("num_inference_steps", 5_0))




      def 							lowerCamelCase     (					self  ,		**_UpperCAmelCase						):
             UpperCAmelCase__	:   List[str]												=	{
                 '''num_train_timesteps''': 1000,
                 '''beta_start''': 0.00_01,
                 '''beta_end''': 0.02,
                 '''beta_schedule''': '''linear''',
                 '''clip_sample''': True,
             }

             config.update(**_UpperCAmelCase						)
             return config




      def 							lowerCamelCase     (					self  ,		**_UpperCAmelCase						):
             UpperCAmelCase__	:   Dict												=	self.scheduler_classes[0]
             UpperCAmelCase__	:   Union[str, Any]												=	self.get_scheduler_config(**_UpperCAmelCase						)
             UpperCAmelCase__	:   str												=	scheduler_class(**_UpperCAmelCase						)

             UpperCAmelCase__							,							UpperCAmelCase__	:   int												=	10, 0.0

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

             scheduler.set_timesteps(_UpperCAmelCase						)

             for t in scheduler.timesteps:
                    UpperCAmelCase__	:   Dict												=	model(_UpperCAmelCase  ,		_UpperCAmelCase						)
                    UpperCAmelCase__	:   int												=	scheduler.step(_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase						).prev_sample

             return sample




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




      def 							lowerCamelCase     (					self						):
             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=_UpperCAmelCase						)

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




      def 							lowerCamelCase     (					self						):
             for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,		[0.0_02, 0.02, 0.2, 2]						):
                    self.check_over_configs(beta_start=_UpperCAmelCase  ,		beta_end=_UpperCAmelCase						)




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




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




      def 							lowerCamelCase     (					self						):
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=_UpperCAmelCase						)




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




      def 							lowerCamelCase     (					self						):
             for rescale_betas_zero_snr in [True, False]:
                    self.check_over_configs(rescale_betas_zero_snr=_UpperCAmelCase						)




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




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




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




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




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   str												=	self.scheduler_classes[0]
             UpperCAmelCase__	:   int												=	self.get_scheduler_config()
             UpperCAmelCase__	:   Optional[Any]												=	scheduler_class(**_UpperCAmelCase						)

             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_47_71						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(980  ,		960						) - 0.3_24_60						)						) < 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_09_79						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(999  ,		998						) - 0.02						)						) < 1E-5




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   str												=	self.full_loop()

             UpperCAmelCase__	:   Dict												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   str												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 1_72.00_67						) < 1E-2
             assert abs(result_mean.item() - 0.22_39_67						) < 1E-3




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Any												=	self.full_loop(prediction_type='''v_prediction'''						)

             UpperCAmelCase__	:   Union[str, Any]												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   Tuple												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 52.53_02						) < 1E-2
             assert abs(result_mean.item() - 0.06_84						) < 1E-3




      def 							lowerCamelCase     (					self						):
             # We specify different beta, so that the first alpha is 0.99
             UpperCAmelCase__	:   str												=	self.full_loop(set_alpha_to_one=_UpperCAmelCase  ,		beta_start=0.01						)
             UpperCAmelCase__	:   List[str]												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   Union[str, Any]												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 1_49.82_95						) < 1E-2
             assert abs(result_mean.item() - 0.19_51						) < 1E-3




      def 							lowerCamelCase     (					self						):
             # We specify different beta, so that the first alpha is 0.99
             UpperCAmelCase__	:   str												=	self.full_loop(set_alpha_to_one=_UpperCAmelCase  ,		beta_start=0.01						)
             UpperCAmelCase__	:   Optional[Any]												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   int												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 1_49.07_84						) < 1E-2
             assert abs(result_mean.item() - 0.19_41						) < 1E-3