


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[int]					  =  (KDPMaDiscreteScheduler,)
							__UpperCAmelCase   :							Union[str, Any]					  =  10

							def    __snake_case (							self	:  List[str]   ,     **lowerCamelCase	:  Any    )     ->   List[str]:
														__snake_case		:      Union[str, Any]										=       {
														    "num_train_timesteps": 1100,
														    "beta_start": 0.00_01,
														    "beta_end": 0.02,
														    "beta_schedule": "linear",
														}

														config.update(**lowerCamelCase    )
														return config

							def    __snake_case (							self	:  Any    )     ->   int:
														for timesteps in [10, 50, 100, 1000]:
																					self.check_over_configs(num_train_timesteps=lowerCamelCase    )

							def    __snake_case (							self	:  Union[str, Any]    )     ->   Optional[Any]:
														for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]   ,     [0.00_02, 0.0_02, 0.02]    ):
																					self.check_over_configs(beta_start=lowerCamelCase   ,     beta_end=lowerCamelCase    )

							def    __snake_case (							self	:  int    )     ->   int:
														for schedule in ["linear", "scaled_linear"]:
																					self.check_over_configs(beta_schedule=lowerCamelCase    )

							def    __snake_case (							self	:  Tuple    )     ->   str:
														for prediction_type in ["epsilon", "v_prediction"]:
																					self.check_over_configs(prediction_type=lowerCamelCase    )

							def    __snake_case (							self	:  Union[str, Any]    )     ->   List[str]:
														__snake_case		:      Union[str, Any]										=       self.scheduler_classes[0]
														__snake_case		:      List[str]										=       self.get_scheduler_config(prediction_type="v_prediction"    )
														__snake_case		:      int										=       scheduler_class(**lowerCamelCase    )

														scheduler.set_timesteps(self.num_inference_steps    )

														__snake_case		:      Any										=       self.dummy_model()
														__snake_case		:      List[str]										=       self.dummy_sample_deter * scheduler.init_noise_sigma
														__snake_case		:      List[Any]										=       sample.to(lowerCamelCase    )

														for i, t in enumerate(scheduler.timesteps    ):
																					__snake_case		:      List[Any]										=       scheduler.scale_model_input(lowerCamelCase   ,     lowerCamelCase    )

																					__snake_case		:      Optional[Any]										=       model(lowerCamelCase   ,     lowerCamelCase    )

																					__snake_case		:      Optional[int]										=       scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    )
																					__snake_case		:      int										=       output.prev_sample

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

														if torch_device in ["cpu", "mps"]:
																					assert abs(result_sum.item() - 4.6934E-07    ) < 1E-2
																					assert abs(result_mean.item() - 6.1112E-10    ) < 1E-3
														else:
																					# CUDA
																					assert abs(result_sum.item() - 4.693428650170972E-07    ) < 1E-2
																					assert abs(result_mean.item() - 0.00_02    ) < 1E-3

							def    __snake_case (							self	:  Optional[int]    )     ->   Tuple:
														if torch_device == "mps":
																					return
														__snake_case		:      Tuple										=       self.scheduler_classes[0]
														__snake_case		:      Optional[int]										=       self.get_scheduler_config()
														__snake_case		:      List[str]										=       scheduler_class(**lowerCamelCase    )

														scheduler.set_timesteps(self.num_inference_steps    )

														__snake_case		:      Optional[int]										=       self.dummy_model()
														__snake_case		:      List[Any]										=       self.dummy_sample_deter * scheduler.init_noise_sigma
														__snake_case		:      List[Any]										=       sample.to(lowerCamelCase    )

														for i, t in enumerate(scheduler.timesteps    ):
																					__snake_case		:      Union[str, Any]										=       scheduler.scale_model_input(lowerCamelCase   ,     lowerCamelCase    )

																					__snake_case		:      Optional[Any]										=       model(lowerCamelCase   ,     lowerCamelCase    )

																					__snake_case		:      List[Any]										=       scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    )
																					__snake_case		:      Optional[Any]										=       output.prev_sample

														__snake_case		:      Dict										=       torch.sum(torch.abs(lowerCamelCase    )    )
														__snake_case		:      List[str]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														if torch_device in ["cpu", "mps"]:
																					assert abs(result_sum.item() - 20.41_25    ) < 1E-2
																					assert abs(result_mean.item() - 0.02_66    ) < 1E-3
														else:
																					# CUDA
																					assert abs(result_sum.item() - 20.41_25    ) < 1E-2
																					assert abs(result_mean.item() - 0.02_66    ) < 1E-3






							def    __snake_case (							self	:  Tuple    )     ->   List[Any]:
														if torch_device == "mps":
																					return
														__snake_case		:      List[Any]										=       self.scheduler_classes[0]
														__snake_case		:      List[str]										=       self.get_scheduler_config()
														__snake_case		:      Tuple										=       scheduler_class(**lowerCamelCase    )

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

														__snake_case		:      Optional[int]										=       self.dummy_model()
														__snake_case		:      Dict										=       self.dummy_sample_deter.to(lowerCamelCase    ) * scheduler.init_noise_sigma

														for t in scheduler.timesteps:
																					__snake_case		:      List[str]										=       scheduler.scale_model_input(lowerCamelCase   ,     lowerCamelCase    )

																					__snake_case		:      List[str]										=       model(lowerCamelCase   ,     lowerCamelCase    )

																					__snake_case		:      List[Any]										=       scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    )
																					__snake_case		:      str										=       output.prev_sample

														__snake_case		:      Optional[Any]										=       torch.sum(torch.abs(lowerCamelCase    )    )
														__snake_case		:      Optional[int]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														if str(lowerCamelCase    ).startswith("cpu"    ):
																					# The following sum varies between 148 and 156 on mps. Why?
																					assert abs(result_sum.item() - 20.41_25    ) < 1E-2
																					assert abs(result_mean.item() - 0.02_66    ) < 1E-3
														else:
																					# CUDA
																					assert abs(result_sum.item() - 20.41_25    ) < 1E-2
																					assert abs(result_mean.item() - 0.02_66    ) < 1E-3





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_snake_case					:		Union[str, Any]      =    datasets.load_iris()

_snake_case					:		Tuple      =    np.array(data["data"])
_snake_case					:		int      =    np.array(data["target"])
_snake_case					:		int      =    data["target_names"]

_snake_case ,				_snake_case ,				_snake_case ,				_snake_case					:		Any      =    train_test_split(X, y)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return np.linalg.norm(np.array(__lowerCamelCase     ) - np.array(__lowerCamelCase     )     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=5     ):
							__snake_case		:      Optional[Any]										=       zip(__lowerCamelCase							,     __lowerCamelCase     )
							# List of distances of all points from the point to be classified
							__snake_case		:      Optional[int]										=       []
							for data_point in data:
														__snake_case		:      Union[str, Any]										=       euclidean_distance(data_point[0]							,     __lowerCamelCase     )
														distances.append((distance, data_point[1])     )
							# Choosing 'k' points with the least distances.
							__snake_case		:      Dict										=       [i[1] for i in sorted(__lowerCamelCase     )[:k]]
							# Most commonly occurring class among them
							# is the class into which the point is classified
							__snake_case		:      Any										=       Counter(__lowerCamelCase     ).most_common(1     )[0][0]
							return classes[result]


if __name__ == "__main__":
			print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))

