

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


a_     =			transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def        lowerCamelCase__       (    _a):
	if isinstance(__UpperCAmelCase							,	torch.Tensor):
		return image
	elif isinstance(__UpperCAmelCase							,	PIL.Image.Image):
		SCREAMING_SNAKE_CASE      :					List[Any]    =				[image]

	SCREAMING_SNAKE_CASE      :					Optional[int]    =				[trans(img.convert("RGB")) for img in image]
	SCREAMING_SNAKE_CASE      :					List[str]    =				torch.stack(__UpperCAmelCase)
	return image






class _UpperCamelCase							(							_SCREAMING_SNAKE_CASE      ):
			'''simple docstring'''







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

				"""simple docstring"""

				super().__init__()

				# make sure scheduler can always be converted to DDIM
				SCREAMING_SNAKE_CASE      :					str    =				DDIMScheduler.from_config(scheduler.config		)

				self.register_modules(unet=_lowerCAmelCase					,					scheduler=_lowerCAmelCase		)
			def 							__UpperCamelCase       (	self							:       Union[str, Any]					,					a							:       Tuple		)							->				Dict:

				"""simple docstring"""

				if strength < 0 or strength > 1:
					raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}"		)
			def 							__UpperCamelCase       (	self							:       Tuple					,					a							:       List[Any]					,					a							:       List[Any]					,					a							:       Tuple		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				min(int(num_inference_steps * strength		)					,					_lowerCAmelCase		)

				SCREAMING_SNAKE_CASE      :					int    =				max(num_inference_steps - init_timestep					,					0		)
				SCREAMING_SNAKE_CASE      :					List[str]    =				self.scheduler.timesteps[t_start:]

				return timesteps, num_inference_steps - t_start
			def 							__UpperCamelCase       (	self							:       Dict					,					a							:       Union[str, Any]					,					a							:       Tuple					,					a							:       Any					,					a							:       Dict					,					a							:       List[Any]					,					a							:       Any=None		)							->				Union[str, Any]:

				"""simple docstring"""

				if not isinstance(_lowerCAmelCase					,					(torch.Tensor, PIL.Image.Image, list)		):
					raise ValueError(
					    F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCAmelCase		)}"		)

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				image.to(device=_lowerCAmelCase					,					dtype=_lowerCAmelCase		)

				if isinstance(_lowerCAmelCase					,					_lowerCAmelCase		) and len(_lowerCAmelCase		) != batch_size:
					raise ValueError(
					    F"You have passed a list of generators of length {len(_lowerCAmelCase		)}, but requested an effective batch"
					    F" size of {batch_size}. Make sure the batch size matches the length of the generators."		)

				SCREAMING_SNAKE_CASE      :					str    =				init_latents.shape
				SCREAMING_SNAKE_CASE      :					Dict    =				randn_tensor(_lowerCAmelCase					,					generator=_lowerCAmelCase					,					device=_lowerCAmelCase					,					dtype=_lowerCAmelCase		)

				# get latents
				print("add noise to latents at timestep"					,					_lowerCAmelCase		)
				SCREAMING_SNAKE_CASE      :					str    =				self.scheduler.add_noise(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				init_latents

				return latents






			@torch.no_grad()
			def __call__(	self							:       str					,					a							:       Union[torch.FloatTensor, PIL.Image.Image] = None					,					a							:       float = 0.8					,					a							:       int = 1					,					a							:       Optional[Union[torch.Generator, List[torch.Generator]]] = None					,					a							:       float = 0.0					,					a							:       int = 50					,					a							:       Optional[bool] = None					,					a							:       Optional[str] = "pil"					,					a							:       bool = True					,					)							->				List[str]:

				"""simple docstring"""

				self.check_inputs(_lowerCAmelCase		)

				# 2. Preprocess image
				SCREAMING_SNAKE_CASE      :					List[Any]    =				preprocess(_lowerCAmelCase		)

				# 3. set timesteps
				self.scheduler.set_timesteps(_lowerCAmelCase					,					device=self.device		)
				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					List[str]    =				self.get_timesteps(_lowerCAmelCase					,					_lowerCAmelCase					,					self.device		)
				SCREAMING_SNAKE_CASE      :					Any    =				timesteps[:1].repeat(_lowerCAmelCase		)

				# 4. Prepare latent variables
				SCREAMING_SNAKE_CASE      :					int    =				self.prepare_latents(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					self.unet.dtype					,					self.device					,					_lowerCAmelCase		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				latents

				# 5. Denoising loop
				for t in self.progress_bar(_lowerCAmelCase		):
					# 1. predict noise model_output
					SCREAMING_SNAKE_CASE      :					List[str]    =				self.unet(_lowerCAmelCase					,					_lowerCAmelCase		).sample

					# 2. predict previous mean of image x_t-1 and add variance depending on eta
					# eta corresponds to η in paper and should be between [0, 1]
					# do x_t -> x_t-1
					SCREAMING_SNAKE_CASE      :					List[Any]    =				self.scheduler.step(
					    _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					eta=_lowerCAmelCase					,					use_clipped_model_output=_lowerCAmelCase					,					generator=_lowerCAmelCase					,					).prev_sample

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

				if not return_dict:
					return (image, latent_timestep.item())

				return ImagePipelineOutput(images=_lowerCAmelCase		)





import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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