




import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class        __snake_case  (_a		):



				def __init__(      self      :		List[Any]		,	_UpperCAmelCase      :		VQModel		,	_UpperCAmelCase      :		UNetaDModel		,	_UpperCAmelCase      :		DDIMScheduler      )  ->    int:





								'''simple docstring'''




								super().__init__()
								self.register_modules(vqvae=_UpperCAmelCase		,	unet=_UpperCAmelCase		,	scheduler=_UpperCAmelCase      )



				@torch.no_grad()
				def __call__(      self      :		Optional[Any]		,	_UpperCAmelCase      :		int = 1		,	_UpperCAmelCase      :		Optional[Union[torch.Generator, List[torch.Generator]]] = None		,	_UpperCAmelCase      :		float = 0.0		,	_UpperCAmelCase      :		int = 50		,	_UpperCAmelCase      :		Optional[str] = "pil"		,	_UpperCAmelCase      :		bool = True		,	**_UpperCAmelCase      :		Any		,	)  ->    Union[Tuple, ImagePipelineOutput]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      randn_tensor(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,	generator=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    str										=      latents.to(self.device      )

								# scale the initial noise by the standard deviation required by the scheduler
								_lowerCAmelCase    :    Union[str, Any]										=      latents * self.scheduler.init_noise_sigma

								self.scheduler.set_timesteps(_UpperCAmelCase      )

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								_lowerCAmelCase    :    int										=      """eta""" in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )

								_lowerCAmelCase    :    Dict										=      {}
								if accepts_eta:
												_lowerCAmelCase    :    Tuple										=      eta

								for t in self.progress_bar(self.scheduler.timesteps      ):
												_lowerCAmelCase    :    Tuple										=      self.scheduler.scale_model_input(_UpperCAmelCase		,	_UpperCAmelCase      )
												# predict the noise residual
												_lowerCAmelCase    :    Dict										=      self.unet(_UpperCAmelCase		,	_UpperCAmelCase      ).sample
												# compute the previous noisy sample x_t -> x_t-1
												_lowerCAmelCase    :    List[Any]										=      self.scheduler.step(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

								# decode the image latents with the VAE
								_lowerCAmelCase    :    str										=      self.vqvae.decode(_UpperCAmelCase      ).sample

								_lowerCAmelCase    :    List[str]										=      (image / 2 + 0.5).clamp(0		,	1      )
								_lowerCAmelCase    :    int										=      image.cpu().permute(0		,	2		,	3		,	1      ).numpy()
								if output_type == "pil":
												_lowerCAmelCase    :    str										=      self.numpy_to_pil(_UpperCAmelCase      )

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=_UpperCAmelCase      )







import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_lowerCamelCase      :  Dict      						=						pd.read_csv(
    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
    "position_salaries.csv"
)
_lowerCamelCase      :  Union[str, Any]      						=						dataset.iloc[:, 1:2].values
_lowerCamelCase      :  Any      						=						dataset.iloc[:, 2].values


_lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase      :  Optional[int]      						=						train_test_split(X, y, test_size=0.2, random_state=0)


_lowerCamelCase      :  Optional[Any]      						=						PolynomialFeatures(degree=4)
_lowerCamelCase      :  Optional[Any]      						=						poly_reg.fit_transform(X)
_lowerCamelCase      :  Dict      						=						LinearRegression()
pol_reg.fit(X_poly, y)




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				plt.scatter(UpperCamelCase_      ,    UpperCamelCase_      ,    color="""red"""       )
				plt.plot(UpperCamelCase_      ,    pol_reg.predict(poly_reg.fit_transform(UpperCamelCase_       )       )      ,    color="""blue"""       )
				plt.title("""Truth or Bluff (Linear Regression)"""       )
				plt.xlabel("""Position level"""       )
				plt.ylabel("""Salary"""       )
				plt.show()


if __name__ == "__main__":
	viz_polymonial()

	# Predicting a new result with Polymonial Regression
	pol_reg.predict(poly_reg.fit_transform([[5.5]]))
	# output should be 132148.43750003

