


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    42
       A__      		=    42
       A__      		=    None







class     _lowercase				(							lowercase__							,						lowercase__):


       """simple docstring"""




       A__      		=    2





       @register_to_config
       def __init__(     self							:    List[str]      ,   __lowerCamelCase							:    float = 0.0_2      ,   __lowerCamelCase							:    float = 100      ,   __lowerCamelCase							:    float = 1.0_0_7      ,   __lowerCamelCase							:    float = 80      ,   __lowerCamelCase							:    float = 0.0_5      ,   __lowerCamelCase							:    float = 50      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						sigma_max

             # setable values
             lowerCamelCase__	:       int					=						None
             lowerCamelCase__	:       np.IntTensor					=						None
             lowerCamelCase__	:       torch.FloatTensor					=						None  # sigma(t_i)





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    torch.FloatTensor      ,   __lowerCamelCase							:    Optional[int] = None ):


             '''simple docstring'''



             return sample





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Union[str, torch.device] = None ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						num_inference_steps
             lowerCamelCase__	:       int					=						np.arange(0      ,   self.num_inference_steps )[::-1].copy()
             lowerCamelCase__	:       Union[str, Any]					=						torch.from_numpy(__lowerCamelCase ).to(__lowerCamelCase )
             lowerCamelCase__	:       Any					=						[
                 (
                     self.config.sigma_max**2
                     * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
                 )
                 for i in self.timesteps
             ]
             lowerCamelCase__	:       Optional[int]					=						torch.tensor(__lowerCamelCase      ,   dtype=torch.floataa      ,   device=__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   __lowerCamelCase							:    torch.FloatTensor      ,   __lowerCamelCase							:    float      ,   __lowerCamelCase							:    Optional[torch.Generator] = None ):


             '''simple docstring'''



             if self.config.s_min <= sigma <= self.config.s_max:
                   lowerCamelCase__	:       Dict					=						min(self.config.s_churn / self.num_inference_steps      ,   2**0.5 - 1 )
             else:
                   lowerCamelCase__	:       Tuple					=						0

             # sample eps ~ N(0, S_noise^2 * I)
             lowerCamelCase__	:       Tuple					=						self.config.s_noise * randn_tensor(sample.shape      ,   generator=__lowerCamelCase ).to(sample.device )
             lowerCamelCase__	:       str					=						sigma + gamma * sigma
             lowerCamelCase__	:       List[Any]					=						sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

             return sample_hat, sigma_hat





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    torch.FloatTensor      ,   __lowerCamelCase							:    float      ,   __lowerCamelCase							:    float      ,   __lowerCamelCase							:    torch.FloatTensor      ,   __lowerCamelCase							:    bool = True      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						sample_hat + sigma_hat * model_output
             lowerCamelCase__	:       Optional[Any]					=						(sample_hat - pred_original_sample) / sigma_hat
             lowerCamelCase__	:       List[Any]					=						sample_hat + (sigma_prev - sigma_hat) * derivative

             if not return_dict:
                   return (sample_prev, derivative)

             return KarrasVeOutput(
                 prev_sample=__lowerCamelCase      ,   derivative=__lowerCamelCase      ,   pred_original_sample=__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    torch.FloatTensor      ,   __lowerCamelCase							:    float      ,   __lowerCamelCase							:    float      ,   __lowerCamelCase							:    torch.FloatTensor      ,   __lowerCamelCase							:    torch.FloatTensor      ,   __lowerCamelCase							:    torch.FloatTensor      ,   __lowerCamelCase							:    bool = True      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						sample_prev + sigma_prev * model_output
             lowerCamelCase__	:       Any					=						(sample_prev - pred_original_sample) / sigma_prev
             lowerCamelCase__	:       List[Any]					=						sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

             if not return_dict:
                   return (sample_prev, derivative)

             return KarrasVeOutput(
                 prev_sample=__lowerCamelCase      ,   derivative=__lowerCamelCase      ,   pred_original_sample=__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Dict ):


             '''simple docstring'''



             raise NotImplementedError()






import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
    print("Googling.....")
    A   :					str	=      "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
    A   :					Optional[int]	=      requests.get(url, headers={"UserAgent": UserAgent().random})
    # res.raise_for_status()
    with open("project1a.html", "wb") as out_file:  # only for knowing the class
        for data in res.iter_content(10000):
            out_file.write(data)
    A   :					int	=      BeautifulSoup(res.text, "html.parser")
    A   :					Any	=      list(soup.select(".eZt8xd"))[:5]

    print(len(links))
    for link in links:
        if link.text == "Maps":
            webbrowser.open(link.get("href"))
        else:
            webbrowser.open(f'https://google.com{link.get("href")}')


