






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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase   ,		lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		str								=						1


     @register_to_config
     def __init__(					self							:						int       ,			UpperCAmelCase_							:						int = 1000       ,			UpperCAmelCase_							:						Optional[Union[np.ndarray, List[float]]] = None   ):
       # set `betas`, `alphas`, `timesteps`
       self.set_timesteps(UpperCAmelCase_   )

       # standard deviation of the initial noise distribution
       SCREAMING_SNAKE_CASE							:   Tuple						    =     1.0

       # For now we only support F-PNDM, i.e. the runge-kutta method
       # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
       # mainly at formula (9), (12), (13) and the Algorithm 2.
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     4

       # running values
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     []


     def 					_A							(					self							:						Tuple       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						Union[str, torch.device] = None   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     num_inference_steps
       SCREAMING_SNAKE_CASE							:   Dict						    =     torch.linspace(1       ,			0       ,			num_inference_steps + 1   )[:-1]
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     torch.cat([steps, torch.tensor([0.0]   )]   )

       if self.config.trained_betas is not None:
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     torch.tensor(self.config.trained_betas       ,			dtype=torch.floataa   )
       else:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     torch.sin(steps * math.pi / 2   ) ** 2

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     (1.0 - self.betas**2) ** 0.5

       SCREAMING_SNAKE_CASE							:   str						    =     (torch.atana(self.betas       ,			self.alphas   ) / math.pi * 2)[:-1]
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     timesteps.to(UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Any						    =     []


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						torch.FloatTensor       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						torch.FloatTensor       ,			UpperCAmelCase_							:						bool = True       ,			):
       if self.num_inference_steps is None:
         raise ValueError(
             "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"   )

       SCREAMING_SNAKE_CASE							:   List[str]						    =     (self.timesteps == timestep).nonzero().item()
       SCREAMING_SNAKE_CASE							:   str						    =     timestep_index + 1

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
       self.ets.append(UpperCAmelCase_   )

       if len(self.ets   ) == 1:
         SCREAMING_SNAKE_CASE							:   List[str]						    =     self.ets[-1]
       elif len(self.ets   ) == 2:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     (3 * self.ets[-1] - self.ets[-2]) / 2
       elif len(self.ets   ) == 3:
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
       else:
         SCREAMING_SNAKE_CASE							:   List[str]						    =     (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

       SCREAMING_SNAKE_CASE							:   Dict						    =     self._get_prev_sample(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )

       if not return_dict:
         return (prev_sample,)

       return SchedulerOutput(prev_sample=UpperCAmelCase_   )


     def 					_A							(					self							:						str       ,			UpperCAmelCase_							:						torch.FloatTensor       ,			*UpperCAmelCase_							:						Optional[int]       ,			**UpperCAmelCase_							:						List[Any]   ):
       return sample


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						List[str]       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						List[Any]       ,			UpperCAmelCase_							:						int   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.alphas[timestep_index]
       SCREAMING_SNAKE_CASE							:   str						    =     self.betas[timestep_index]

       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.alphas[prev_timestep_index]
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.betas[prev_timestep_index]

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     (sample - sigma * ets) / max(UpperCAmelCase_       ,			1E-8   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     next_alpha * pred + ets * next_sigma

       return prev_sample






     def __len__(					self							:						List[Any]   ):
       return self.config.num_train_timesteps








'''simple docstring'''



import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
						print("""Googling.....""")
						_lowercase          =						"""https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
						_lowercase          =						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)
						_lowercase          =						BeautifulSoup(res.text, """html.parser""")
						_lowercase          =						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')}""")
