





'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


__UpperCamelCase					    =	logging.get_logger(__name__)  # pylint: disable=invalid-name




class  _A (					__lowercase		):

		def __init__(   self					:   Optional[Any]  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   int			)					->    Optional[int]:


					"""simple docstring"""

					super().__init__()
					self.register_modules(unet=__magic_name__  ,      scheduler=__magic_name__			)

		@torch.no_grad()
		def __call__(   self					:   Any  ,      __magic_name__					:   int = 1  ,      __magic_name__					:   int = 1_00  ,      __magic_name__					:   Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,      __magic_name__					:   Optional[float] = None  ,      __magic_name__					:   bool = True  ,      )					->    Union[AudioPipelineOutput, Tuple]:


					"""simple docstring"""

					if audio_length_in_s is None:
								__snake_case					:      Optional[Any]							      =     self.unet.config.sample_size / self.unet.config.sample_rate

					__snake_case					:      Union[str, Any]							      =     audio_length_in_s * self.unet.config.sample_rate

					__snake_case					:      List[str]							      =     2 ** len(self.unet.up_blocks			)
					if sample_size < 3 * down_scale_factor:
								raise ValueError(
								    f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
								    f''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''			)

					__snake_case					:      List[str]							      =     int(__magic_name__			)
					if sample_size % down_scale_factor != 0:
								__snake_case					:      Tuple							      =     (
								    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
								) * down_scale_factor
								logger.info(
								    f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
								    f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
								    """ process."""			)
					__snake_case					:      List[str]							      =     int(__magic_name__			)

					__snake_case					:      Optional[int]							      =     next(iter(self.unet.parameters()			)			).dtype
					__snake_case					:      Optional[Any]							      =     (batch_size, self.unet.config.in_channels, sample_size)
					if isinstance(__magic_name__  ,      __magic_name__			) and len(__magic_name__			) != batch_size:
								raise ValueError(
								    f'''You have passed a list of generators of length {len(__magic_name__			)}, but requested an effective batch'''
								    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''			)

					__snake_case					:      List[Any]							      =     randn_tensor(__magic_name__  ,      generator=__magic_name__  ,      device=self.device  ,      dtype=__magic_name__			)

					# set step values
					self.scheduler.set_timesteps(__magic_name__  ,      device=audio.device			)
					__snake_case					:      Union[str, Any]							      =     self.scheduler.timesteps.to(__magic_name__			)

					for t in self.progress_bar(self.scheduler.timesteps			):
								# 1. predict noise model_output
								__snake_case					:      Tuple							      =     self.unet(__magic_name__  ,      __magic_name__			).sample

								# 2. compute previous image: x_t -> t_t-1
								__snake_case					:      Optional[int]							      =     self.scheduler.step(__magic_name__  ,      __magic_name__  ,      __magic_name__			).prev_sample

					__snake_case					:      int							      =     audio.clamp(-1  ,      1			).float().cpu().numpy()

					__snake_case					:      str							      =     audio[:, :, :original_sample_size]

					if not return_dict:
								return (audio,)

					return AudioPipelineOutput(audios=__magic_name__			)





'''simple docstring'''




import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
  print('Googling.....')
  _a					:			List[Any]							=    'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
  _a					:			Dict							=    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(10_000):
      out_file.write(data)
  _a					:			Any							=    BeautifulSoup(res.text, 'html.parser')
  _a					:			Optional[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')}''')


