


"""simple docstring"""






from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class      lowerCamelCase__   :







					"""simple docstring"""


					__a	       =							42  # [batch_size x 3]
					__a	       =							42  # [batch_size x 3]
					__a	       =							42  # [batch_size x 3]
					__a	       =							42  # [batch_size x 3]
					__a	       =							42
					__a	       =							42
					__a	       =							42
					__a	       =							42
					__a	       =							42






					def lowerCamelCase__							(       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 lowerCamelCase__							(       self    : Optional[int]  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					torch.arange(self.height * self.width  )
												__UpperCAmelCase   :						Union[str, Any]      							=					torch.stack(
												    [
												        pixel_indices % self.width,
												        torch.div(_lowercase ,       self.width ,       rounding_mode="""trunc"""  ),
												    ] ,       axis=1 ,       )
												return coords






					@property
					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase							,*__UpperCAmelCase   :						Optional[int]      							=					self.shape
												__UpperCAmelCase   :						Optional[int]      							=					int(np.prod(_lowercase  )  )

												__UpperCAmelCase   :						Any      							=					self.get_image_coords()
												__UpperCAmelCase   :						Optional[Any]      							=					torch.broadcast_to(coords.unsqueeze(0  ) ,       [batch_size * inner_batch_size, *coords.shape]  )
												__UpperCAmelCase   :						Union[str, Any]      							=					self.get_camera_rays(_lowercase  )

												__UpperCAmelCase   :						Dict      							=					rays.view(_lowercase ,       inner_batch_size * self.height * self.width ,       2 ,       3  )

												return rays






					def lowerCamelCase__							(       self    : str ,       UpperCamelCase    : torch.Tensor  ):
												'''simple docstring'''






												__UpperCAmelCase							,*__UpperCAmelCase							,__UpperCAmelCase   :						List[str]      							=					coords.shape
												assert n_coords == 2
												assert batch_size == self.origin.shape[0]

												__UpperCAmelCase   :						List[str]      							=					coords.view(_lowercase ,       -1 ,       2  )

												__UpperCAmelCase   :						Dict      							=					self.resolution()
												__UpperCAmelCase   :						Tuple      							=					self.fov()

												__UpperCAmelCase   :						Union[str, Any]      							=					(flat.float() / (res - 1)) * 2 - 1
												__UpperCAmelCase   :						int      							=					fracs * torch.tan(fov / 2  )

												__UpperCAmelCase   :						Any      							=					fracs.view(_lowercase ,       -1 ,       2  )
												__UpperCAmelCase   :						str      							=					(
												    self.z.view(_lowercase ,       1 ,       3  )
												    + self.x.view(_lowercase ,       1 ,       3  ) * fracs[:, :, :1]
												    + self.y.view(_lowercase ,       1 ,       3  ) * fracs[:, :, 1:]
												)
												__UpperCAmelCase   :						str      							=					directions / directions.norm(dim=-1 ,       keepdim=_lowercase  )
												__UpperCAmelCase   :						str      							=					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 lowerCamelCase__							(       self    : Dict ,       UpperCamelCase    : int ,       UpperCamelCase    : 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      lowerCamelCase       (     _UpperCamelCase		:      int							)		->       DifferentiableProjectiveCamera:

							'''simple docstring'''



							__UpperCAmelCase   :						List[str]      							=					[]
							__UpperCAmelCase   :						List[str]      							=					[]
							__UpperCAmelCase   :						Optional[int]      							=					[]
							__UpperCAmelCase   :						Dict      							=					[]
							for theta in np.linspace(0       ,			2 * np.pi       ,			num=2_0							):
														__UpperCAmelCase   :						List[Any]      							=					np.array([np.sin(__UpperCamelCase							), np.cos(__UpperCamelCase							), -0.5]							)
														z /= np.sqrt(np.sum(z**2							)							)
														__UpperCAmelCase   :						str      							=					-z * 4
														__UpperCAmelCase   :						int      							=					np.array([np.cos(__UpperCamelCase							), -np.sin(__UpperCamelCase							), 0.0]							)
														__UpperCAmelCase   :						List[str]      							=					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							))       ,			)

import socket



def 	__SCREAMING_SNAKE_CASE  (					)							->					Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      socket.socket(socket.AF_INET ,     socket.SOCK_STREAM	)
  SCREAMING_SNAKE_CASE__										=      socket.gethostname()
  SCREAMING_SNAKE_CASE__										=      1_23_12

  sock.connect((host, port)	)
  sock.send(B"""Hello server!"""	)

  with open("""Received_file""" ,     """wb"""	) as out_file:
    print("""File opened"""	)
    print("""Receiving data..."""	)
    while True:
      SCREAMING_SNAKE_CASE__										=      sock.recv(10_24	)
      if not data:
        break
      out_file.write(__UpperCamelCase	)

  print("""Successfully received the file"""	)
  sock.close()
  print("""Connection closed"""	)


if __name__ == "__main__":
   main()
