



"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class 					a	(  UpperCAmelCase__	,      UpperCAmelCase__       ):
  UpperCamelCase	:				Tuple     						=					1



  @register_to_config
  def __init__(						self :					Tuple       ,		lowerCAmelCase :					List[str]=2000       ,		lowerCAmelCase :					List[Any]=0.1       ,		lowerCAmelCase :					List[str]=20       ,		lowerCAmelCase :					Any=1E-3		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=None
     SCREAMING_SNAKE_CASE_: Tuple     			=None
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=None



  def 	lowerCamelCase__       (						self :					Dict       ,		lowerCAmelCase :					Optional[int]       ,		lowerCAmelCase :					Union[str, torch.device] = None		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=torch.linspace(1       ,		self.config.sampling_eps       ,		lowerCAmelCase       ,		device=lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					Union[str, Any]       ,		lowerCAmelCase :					Dict       ,		lowerCAmelCase :					List[str]       ,		lowerCAmelCase :					Union[str, Any]=None		)     ->					int:


     '''simple docstring'''




     if self.timesteps is None:
        raise ValueError(
            """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""		)

     # TODO(Patrick) better comments + non-PyTorch
     # postprocess model score
     SCREAMING_SNAKE_CASE_: str     			=(
         -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
     )
     SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff		)		)
     SCREAMING_SNAKE_CASE_: int     			=std.flatten()
     while len(std.shape		) < len(score.shape		):
        SCREAMING_SNAKE_CASE_: Optional[int]     			=std.unsqueeze(-1		)
     SCREAMING_SNAKE_CASE_: Dict     			=-score / std

     # compute
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=-1.0 / len(self.timesteps		)

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
     SCREAMING_SNAKE_CASE_: Any     			=beta_t.flatten()
     while len(beta_t.shape		) < len(x.shape		):
        SCREAMING_SNAKE_CASE_: List[str]     			=beta_t.unsqueeze(-1		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=-0.5 * beta_t * x

     SCREAMING_SNAKE_CASE_: Tuple     			=torch.sqrt(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: str     			=drift - diffusion**2 * score
     SCREAMING_SNAKE_CASE_: Tuple     			=x + drift * dt

     # add noise
     SCREAMING_SNAKE_CASE_: Tuple     			=randn_tensor(x.shape       ,		layout=x.layout       ,		generator=lowerCAmelCase       ,		device=x.device       ,		dtype=x.dtype		)
     SCREAMING_SNAKE_CASE_: Tuple     			=x_mean + diffusion * math.sqrt(-dt		) * noise

     return x, x_mean





  def __len__(						self :					Tuple		)     ->					str:


     '''simple docstring'''




     return self.config.num_train_timesteps








"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier





def      __magic_name__				(							lowercase ):
   return (data["data"], data["target"])





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=XGBClassifier()
   classifier.fit(lowercase							,			lowercase )
   return classifier





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=load_iris()
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=data_handling(lowercase )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=train_test_split(
       lowercase							,			lowercase							,			test_size=0.25 )

   SCREAMING_SNAKE_CASE_: Tuple     			=iris["""target_names"""]

   # Create an XGBoost Classifier from the training data
   SCREAMING_SNAKE_CASE_: Optional[int]     			=xgboost(lowercase							,			lowercase )

   # Display the confusion matrix of the classifier with both training and test sets
   ConfusionMatrixDisplay.from_estimator(
       lowercase							,			lowercase							,			lowercase							,			display_labels=lowercase							,			cmap="""Blues"""							,			normalize="""true"""							,			)
   plt.title("""Normalized Confusion Matrix - IRIS Dataset""" )
   plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()



