






import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler


class 		lowerCAmelCase_							(     unittest.TestCase   ):
     # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
     __lowerCamelCase					:						Optional[int]     =    (ScoreSdeVeScheduler,)
     __lowerCamelCase					:						str     =    ()





     @property
     def 	lowerCamelCase       (  self     )		->							Optional[Any]:
        lowerCamelCase											=4
        lowerCamelCase											=3
        lowerCamelCase											=8
        lowerCamelCase											=8

        lowerCamelCase											=torch.rand((batch_size, num_channels, height, width)     )

        return sample





     @property
     def 	lowerCamelCase       (  self     )		->							List[Any]:
        lowerCamelCase											=4
        lowerCamelCase											=3
        lowerCamelCase											=8
        lowerCamelCase											=8

        lowerCamelCase											=batch_size * num_channels * height * width
        lowerCamelCase											=torch.arange(_UpperCAmelCase     )
        lowerCamelCase											=sample.reshape(_UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase     )
        lowerCamelCase											=sample / num_elems
        lowerCamelCase											=sample.permute(3       ,   0       ,   1       ,   2     )

        return sample





     def 	lowerCamelCase       (  self     )		->							Dict:
        def model(_UpperCAmelCase       ,   _UpperCAmelCase       ,   *_UpperCAmelCase     ):
           return sample * t / (t + 1)

        return model





     def 	lowerCamelCase       (  self       ,   **_UpperCAmelCase     )		->							str:
        lowerCamelCase											={
            """num_train_timesteps""": 2000,
            """snr""": 0.15,
            """sigma_min""": 0.01,
            """sigma_max""": 1348,
            """sampling_eps""": 1E-5,
        }

        config.update(**_UpperCAmelCase     )
        return config





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase=0       ,   **_UpperCAmelCase     )		->							Optional[Any]:
        lowerCamelCase											=dict(self.forward_default_kwargs     )

        for scheduler_class in self.scheduler_classes:
           lowerCamelCase											=self.dummy_sample
           lowerCamelCase											=0.1 * sample

           lowerCamelCase											=self.get_scheduler_config(**_UpperCAmelCase     )
           lowerCamelCase											=scheduler_class(**_UpperCAmelCase     )

           with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(_UpperCAmelCase     )
              lowerCamelCase											=scheduler_class.from_pretrained(_UpperCAmelCase     )

           lowerCamelCase											=scheduler.step_pred(
               _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   generator=torch.manual_seed(0     )       ,   **_UpperCAmelCase     ).prev_sample
           lowerCamelCase											=new_scheduler.step_pred(
               _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   generator=torch.manual_seed(0     )       ,   **_UpperCAmelCase     ).prev_sample

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

           lowerCamelCase											=scheduler.step_correct(_UpperCAmelCase       ,   _UpperCAmelCase       ,   generator=torch.manual_seed(0     )       ,   **_UpperCAmelCase     ).prev_sample
           lowerCamelCase											=new_scheduler.step_correct(
               _UpperCAmelCase       ,   _UpperCAmelCase       ,   generator=torch.manual_seed(0     )       ,   **_UpperCAmelCase     ).prev_sample

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





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase=0       ,   **_UpperCAmelCase     )		->							Optional[int]:
        lowerCamelCase											=dict(self.forward_default_kwargs     )
        kwargs.update(_UpperCAmelCase     )

        for scheduler_class in self.scheduler_classes:
           lowerCamelCase											=self.dummy_sample
           lowerCamelCase											=0.1 * sample

           lowerCamelCase											=self.get_scheduler_config()
           lowerCamelCase											=scheduler_class(**_UpperCAmelCase     )

           with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(_UpperCAmelCase     )
              lowerCamelCase											=scheduler_class.from_pretrained(_UpperCAmelCase     )

           lowerCamelCase											=scheduler.step_pred(
               _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   generator=torch.manual_seed(0     )       ,   **_UpperCAmelCase     ).prev_sample
           lowerCamelCase											=new_scheduler.step_pred(
               _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   generator=torch.manual_seed(0     )       ,   **_UpperCAmelCase     ).prev_sample

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

           lowerCamelCase											=scheduler.step_correct(_UpperCAmelCase       ,   _UpperCAmelCase       ,   generator=torch.manual_seed(0     )       ,   **_UpperCAmelCase     ).prev_sample
           lowerCamelCase											=new_scheduler.step_correct(
               _UpperCAmelCase       ,   _UpperCAmelCase       ,   generator=torch.manual_seed(0     )       ,   **_UpperCAmelCase     ).prev_sample

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





     def 	lowerCamelCase       (  self     )		->							Optional[int]:
        for timesteps in [10, 100, 1000]:
           self.check_over_configs(num_train_timesteps=_UpperCAmelCase     )





     def 	lowerCamelCase       (  self     )		->							Union[str, Any]:
        for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]       ,   [1, 100, 1000]     ):
           self.check_over_configs(sigma_min=_UpperCAmelCase       ,   sigma_max=_UpperCAmelCase     )





     def 	lowerCamelCase       (  self     )		->							List[Any]:
        for t in [0.1, 0.5, 0.75]:
           self.check_over_forward(time_step=_UpperCAmelCase     )





     def 	lowerCamelCase       (  self     )		->							str:
        lowerCamelCase											=dict(self.forward_default_kwargs     )

        lowerCamelCase											=self.scheduler_classes[0]
        lowerCamelCase											=self.get_scheduler_config()
        lowerCamelCase											=scheduler_class(**_UpperCAmelCase     )

        lowerCamelCase											=3

        lowerCamelCase											=self.dummy_model()
        lowerCamelCase											=self.dummy_sample_deter

        scheduler.set_sigmas(_UpperCAmelCase     )
        scheduler.set_timesteps(_UpperCAmelCase     )
        lowerCamelCase											=torch.manual_seed(0     )

        for i, t in enumerate(scheduler.timesteps     ):
           lowerCamelCase											=scheduler.sigmas[i]

           for _ in range(scheduler.config.correct_steps     ):
              with torch.no_grad():
                 lowerCamelCase											=model(_UpperCAmelCase       ,   _UpperCAmelCase     )
              lowerCamelCase											=scheduler.step_correct(_UpperCAmelCase       ,   _UpperCAmelCase       ,   generator=_UpperCAmelCase       ,   **_UpperCAmelCase     ).prev_sample

           with torch.no_grad():
              lowerCamelCase											=model(_UpperCAmelCase       ,   _UpperCAmelCase     )

           lowerCamelCase											=scheduler.step_pred(_UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   generator=_UpperCAmelCase       ,   **_UpperCAmelCase     )
           lowerCamelCase							,					lowerCamelCase											=output.prev_sample, output.prev_sample_mean

        lowerCamelCase											=torch.sum(torch.abs(_UpperCAmelCase     )     )
        lowerCamelCase											=torch.mean(torch.abs(_UpperCAmelCase     )     )

        assert np.isclose(result_sum.item()       ,   14372758528.0     )
        assert np.isclose(result_mean.item()       ,   18714530.0     )





     def 	lowerCamelCase       (  self     )		->							List[str]:
        lowerCamelCase											=dict(self.forward_default_kwargs     )

        lowerCamelCase											=kwargs.pop("""num_inference_steps"""       ,   _UpperCAmelCase     )

        for scheduler_class in self.scheduler_classes:
           lowerCamelCase											=self.get_scheduler_config()
           lowerCamelCase											=scheduler_class(**_UpperCAmelCase     )

           lowerCamelCase											=self.dummy_sample
           lowerCamelCase											=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"""     ):
              lowerCamelCase											=num_inference_steps

           lowerCamelCase											=scheduler.step_pred(_UpperCAmelCase       ,   0       ,   _UpperCAmelCase       ,   generator=torch.manual_seed(0     )       ,   **_UpperCAmelCase     ).prev_sample
           lowerCamelCase											=scheduler.step_pred(_UpperCAmelCase       ,   1       ,   _UpperCAmelCase       ,   generator=torch.manual_seed(0     )       ,   **_UpperCAmelCase     ).prev_sample

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







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

# Load iris file
_UpperCAmelCase				:	Optional[int]    						=    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				:	List[Any]    						=    train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

_UpperCAmelCase				:	Optional[int]    						=    KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

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

_UpperCAmelCase				:	Tuple    						=    knn.predict(X_new)

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