

'''simple docstring'''

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


SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE							:				Dict     	=			train_test_split(X, y, test_size=0.2, random_state=0)


SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			PolynomialFeatures(degree=4)
SCREAMING_SNAKE_CASE							:				List[str]     	=			poly_reg.fit_transform(X)
SCREAMING_SNAKE_CASE							:				List[str]     	=			LinearRegression()
pol_reg.fit(X_poly, y)




def 							_UpperCamelCase				(   )      -> List[Any]:
							plt.scatter(lowerCAmelCase__		,lowerCAmelCase__		,color='red'   )
							plt.plot(lowerCAmelCase__		,pol_reg.predict(poly_reg.fit_transform(lowerCAmelCase__   )   )		,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








'''simple docstring'''

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     (      lowercase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											super().__init__()
											self.register_modules(vqvae=_lowercase,     unet=_lowercase,     scheduler=_lowercase       )





				@torch.no_grad()
				def __call__(    self,     _lowercase = 1,     _lowercase = None,     _lowercase = 0.0,     _lowercase = 50,     _lowercase = "pil",     _lowercase = True,     **_lowercase,     )     ->							Union[Tuple, ImagePipelineOutput]:
											SCREAMING_SNAKE_CASE_				=				randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),     generator=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				latents.to(self.device       )

											# scale the initial noise by the standard deviation required by the scheduler
											SCREAMING_SNAKE_CASE_				=				latents * self.scheduler.init_noise_sigma

											self.scheduler.set_timesteps(_lowercase       )

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

											SCREAMING_SNAKE_CASE_				=				{}
											if accepts_eta:
																		SCREAMING_SNAKE_CASE_				=				eta

											for t in self.progress_bar(self.scheduler.timesteps       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.scale_model_input(_lowercase,     _lowercase       )
																		# predict the noise residual
																		SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase       ).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.step(_lowercase,     _lowercase,     _lowercase,     **_lowercase       ).prev_sample

											# decode the image latents with the VAE
											SCREAMING_SNAKE_CASE_				=				self.vqvae.decode(_lowercase       ).sample

											SCREAMING_SNAKE_CASE_				=				(image / 2 + 0.5).clamp(0,     1       )
											SCREAMING_SNAKE_CASE_				=				image.cpu().permute(0,     2,     3,     1       ).numpy()
											if output_type == "pil":
																		SCREAMING_SNAKE_CASE_				=				self.numpy_to_pil(_lowercase       )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_lowercase       )





