from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class __snake_case				:
   lowerCAmelCase_														=							42  # [batch_size x 3]
   lowerCAmelCase_														=							42  # [batch_size x 3]
   lowerCAmelCase_														=							42  # [batch_size x 3]
   lowerCAmelCase_														=							42  # [batch_size x 3]
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
     assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
     assert len(self.x.shape      ) == len(self.y.shape      ) == len(self.z.shape      ) == len(self.origin.shape      ) == 2




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     return torch.from_numpy(np.array([self.width, self.height]       ,	dtype=np.floataa      )      )




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     return torch.from_numpy(np.array([self.x_fov, self.y_fov]       ,	dtype=np.floataa      )      )




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      torch.arange(self.height * self.width      )
     SCREAMING_SNAKE_CASE__										=      torch.stack(
         [
             pixel_indices % self.width,
             torch.div(_lowercase       ,	self.width       ,	rounding_mode="""trunc"""      ),
         ]       ,	axis=1       ,	)
     return coords




   @property
   def 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     *SCREAMING_SNAKE_CASE__										=      self.shape
     SCREAMING_SNAKE_CASE__										=      int(np.prod(_lowercase      )      )

     SCREAMING_SNAKE_CASE__										=      self.get_image_coords()
     SCREAMING_SNAKE_CASE__										=      torch.broadcast_to(coords.unsqueeze(0      )       ,	[batch_size * inner_batch_size, *coords.shape]      )
     SCREAMING_SNAKE_CASE__										=      self.get_camera_rays(_lowercase      )

     SCREAMING_SNAKE_CASE__										=      rays.view(_lowercase       ,	inner_batch_size * self.height * self.width       ,	2       ,	3      )

     return rays




   def 	__a						(    self			:				str       ,	_lowercase			:				torch.Tensor      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     *SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      coords.shape
     assert n_coords == 2
     assert batch_size == self.origin.shape[0]

     SCREAMING_SNAKE_CASE__										=      coords.view(_lowercase       ,	-1       ,	2      )

     SCREAMING_SNAKE_CASE__										=      self.resolution()
     SCREAMING_SNAKE_CASE__										=      self.fov()

     SCREAMING_SNAKE_CASE__										=      (flat.float() / (res - 1)) * 2 - 1
     SCREAMING_SNAKE_CASE__										=      fracs * torch.tan(fov / 2      )

     SCREAMING_SNAKE_CASE__										=      fracs.view(_lowercase       ,	-1       ,	2      )
     SCREAMING_SNAKE_CASE__										=      (
         self.z.view(_lowercase       ,	1       ,	3      )
         + self.x.view(_lowercase       ,	1       ,	3      ) * fracs[:, :, :1]
         + self.y.view(_lowercase       ,	1       ,	3      ) * fracs[:, :, 1:]
     )
     SCREAMING_SNAKE_CASE__										=      directions / directions.norm(dim=-1       ,	keepdim=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      torch.stack(
         [
             torch.broadcast_to(self.origin.view(_lowercase       ,	1       ,	3      )       ,	[batch_size, directions.shape[1], 3]      ),
             directions,
         ]       ,	dim=2       ,	)
     return rays.view(_lowercase       ,	*_lowercase       ,	2       ,	3      )




   def 	__a						(    self			:				Dict       ,	_lowercase			:				int       ,	_lowercase			:				int      ):



     """simple docstring"""






     assert width * self.height == height * self.width, "The aspect ratio should not change."
     return DifferentiableProjectiveCamera(
         origin=self.origin       ,	x=self.x       ,	y=self.y       ,	z=self.z       ,	width=_lowercase       ,	height=_lowercase       ,	x_fov=self.x_fov       ,	y_fov=self.y_fov       ,	)





def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int	)							->					DifferentiableProjectiveCamera:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      []
  SCREAMING_SNAKE_CASE__										=      []
  SCREAMING_SNAKE_CASE__										=      []
  SCREAMING_SNAKE_CASE__										=      []
  for theta in np.linspace(0 ,     2 * np.pi ,     num=20	):
    SCREAMING_SNAKE_CASE__										=      np.array([np.sin(__UpperCamelCase	), np.cos(__UpperCamelCase	), -0.5]	)
    z /= np.sqrt(np.sum(z**2	)	)
    SCREAMING_SNAKE_CASE__										=      -z * 4
    SCREAMING_SNAKE_CASE__										=      np.array([np.cos(__UpperCamelCase	), -np.sin(__UpperCamelCase	), 0.0]	)
    SCREAMING_SNAKE_CASE__										=      np.cross(__UpperCamelCase ,     __UpperCamelCase	)
    origins.append(__UpperCamelCase	)
    xs.append(__UpperCamelCase	)
    ys.append(__UpperCamelCase	)
    zs.append(__UpperCamelCase	)
  return DifferentiableProjectiveCamera(
      origin=torch.from_numpy(np.stack(__UpperCamelCase ,     axis=0	)	).float() ,     x=torch.from_numpy(np.stack(__UpperCamelCase ,     axis=0	)	).float() ,     y=torch.from_numpy(np.stack(__UpperCamelCase ,     axis=0	)	).float() ,     z=torch.from_numpy(np.stack(__UpperCamelCase ,     axis=0	)	).float() ,     width=__UpperCamelCase ,     height=__UpperCamelCase ,     x_fov=0.7 ,     y_fov=0.7 ,     shape=(1, len(__UpperCamelCase	)) ,     )

from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
   __lowerCamelCase			: str							   =		input('''Enter image url: ''').strip()
   print(F"""Downloading image from {url} ...""")
   __lowerCamelCase			: Any							   =		BeautifulSoup(requests.get(url).content, '''html.parser''')
   # The image URL is in the content field of the first meta tag with property og:image
   __lowerCamelCase			: List[Any]							   =		soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
   __lowerCamelCase			: Tuple							   =		requests.get(image_url).content
   __lowerCamelCase			: Union[str, Any]							   =		F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
   with open(file_name, '''wb''') as fp:
      fp.write(image_data)
   print(F"""Done. Image saved to disk as {file_name}.""")
