
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




@dataclass
class      __A:

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      42  # [batch_size x 3]
  SCREAMING_SNAKE_CASE__         =      42  # [batch_size x 3]
  SCREAMING_SNAKE_CASE__         =      42  # [batch_size x 3]
  SCREAMING_SNAKE_CASE__         =      42  # [batch_size x 3]
  SCREAMING_SNAKE_CASE__         =      42
  SCREAMING_SNAKE_CASE__         =      42
  SCREAMING_SNAKE_CASE__         =      42
  SCREAMING_SNAKE_CASE__         =      42
  SCREAMING_SNAKE_CASE__         =      42







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







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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    torch.arange(self.height * self.width						)
       UpperCamelCase__				=    torch.stack(
           [
               pixel_indices % self.width,
               torch.div(lowerCamelCase__	,      self.width	,      rounding_mode="""trunc"""						),
           ]	,      axis=1	,      )
       return coords







  @property
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	*UpperCamelCase__				=    self.shape
       UpperCamelCase__				=    int(np.prod(lowerCamelCase__						)						)

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

       UpperCamelCase__				=    rays.view(lowerCamelCase__	,      inner_batch_size * self.height * self.width	,      2	,      3						)

       return rays







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__		,	*UpperCamelCase__		,	UpperCamelCase__				=    coords.shape
       assert n_coords == 2
       assert batch_size == self.origin.shape[0]

       UpperCamelCase__				=    coords.view(lowerCamelCase__	,      -1	,      2						)

       UpperCamelCase__				=    self.resolution()
       UpperCamelCase__				=    self.fov()

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

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







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       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=lowerCamelCase__	,      height=lowerCamelCase__	,      x_fov=self.x_fov	,      y_fov=self.y_fov	,      )






def 						__magic_name__			(   __a      :	int						):



     '''simple docstring'''




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







import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows






def 						__magic_name__			(   __a      :	Any						):  # picklable for multiprocessing



     '''simple docstring'''




     return i + 1






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def 						__magic_name__			(   ):



     '''simple docstring'''




     with parallel_backend("""spark"""						):
          assert ParallelBackendConfig.backend_name == "spark"

     UpperCamelCase__				=    [1, 2, 3]
     with pytest.raises(__a						):
          with parallel_backend("""unsupported backend"""						):
               map_nested(__a	,		__a	,		num_proc=2						)

     with pytest.raises(__a						):
          with parallel_backend("""unsupported backend"""						):
               map_nested(__a	,		__a	,		num_proc=-1						)






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc"""	,		[2, -1]						)
def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    [1, 2]
     UpperCamelCase__				=    {"""a""": 1, """b""": 2}
     UpperCamelCase__				=    {"""a""": [1, 2], """b""": [3, 4]}
     UpperCamelCase__				=    {"""a""": {"""1""": 1}, """b""": 2}
     UpperCamelCase__				=    {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
     UpperCamelCase__				=    [2, 3]
     UpperCamelCase__				=    {"""a""": 2, """b""": 3}
     UpperCamelCase__				=    {"""a""": [2, 3], """b""": [4, 5]}
     UpperCamelCase__				=    {"""a""": {"""1""": 2}, """b""": 3}
     UpperCamelCase__				=    {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}

     with parallel_backend("""spark"""						):
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa





