
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




@dataclass
class        UpperCAmelCase_			:


  '''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 							_lowercase							( self			: Optional[Any]    )			->	Union[str, 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 							_lowercase							( self			: Union[str, Any]    )			->	Optional[Any]:







    """simple docstring"""






    return torch.from_numpy(np.array([self.width, self.height]			,							dtype=np.floataa    )    )
  def 							_lowercase							( self			: str    )			->	Tuple:







    """simple docstring"""






    return torch.from_numpy(np.array([self.x_fov, self.y_fov]			,							dtype=np.floataa    )    )
  def 							_lowercase							( self			: List[str]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       torch.arange(self.height * self.width    )
    __magic_name__				     =       torch.stack(
        [
            pixel_indices % self.width,
            torch.div(snake_case_			,							self.width			,							rounding_mode="""trunc"""    ),
        ]			,							axis=1			,							)
    return coords
  @property
  def 							_lowercase							( self			: Tuple    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       self.shape
    __magic_name__				     =       int(np.prod(snake_case_    )    )

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

    __magic_name__				     =       rays.view(snake_case_			,							inner_batch_size * self.height * self.width			,							2			,							3    )

    return rays
  def 							_lowercase							( self			: Any			,							UpperCamelCase__			: torch.Tensor    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       coords.shape
    assert n_coords == 2
    assert batch_size == self.origin.shape[0]

    __magic_name__				     =       coords.view(snake_case_			,							-1			,							2    )

    __magic_name__				     =       self.resolution()
    __magic_name__				     =       self.fov()

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

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





  def 							_lowercase							( self			: Union[str, Any]			,							UpperCamelCase__			: int			,							UpperCamelCase__			: int    )			->	Optional[Any]:







    """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=snake_case_			,							height=snake_case_			,							x_fov=self.x_fov			,							y_fov=self.y_fov			,							)







def a__    (       A_					):






  '''simple docstring'''





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






'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a          =		logging.get_logger(__name__)

__a          =		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		"glpn"




    def __init__(    self					:     Optional[Any] ,     snake_case_					:     List[str]=3 ,     snake_case_					:     Dict=4 ,     snake_case_					:     List[Any]=[2, 2, 2, 2] ,     snake_case_					:     int=[8, 4, 2, 1] ,     snake_case_					:     List[str]=[32, 64, 160, 256] ,     snake_case_					:     Tuple=[7, 3, 3, 3] ,     snake_case_					:     List[Any]=[4, 2, 2, 2] ,     snake_case_					:     Tuple=[1, 2, 5, 8] ,     snake_case_					:     List[str]=[4, 4, 4, 4] ,     snake_case_					:     Optional[int]="gelu" ,     snake_case_					:     Dict=0.0 ,     snake_case_					:     Union[str, Any]=0.0 ,     snake_case_					:     List[Any]=0.02 ,     snake_case_					:     Tuple=0.1 ,     snake_case_					:     Any=1E-6 ,     snake_case_					:     Dict=64 ,     snake_case_					:     Tuple=10 ,     snake_case_					:     List[Any]=-1 ,     **snake_case_					:     Optional[Any] ,     ):
          super().__init__(**snake_case_						)

          snake_case__  :    Optional[Any]    						=      num_channels
          snake_case__  :    Dict    						=      num_encoder_blocks
          snake_case__  :    Tuple    						=      depths
          snake_case__  :    Union[str, Any]    						=      sr_ratios
          snake_case__  :    Tuple    						=      hidden_sizes
          snake_case__  :    Optional[Any]    						=      patch_sizes
          snake_case__  :    int    						=      strides
          snake_case__  :    List[Any]    						=      mlp_ratios
          snake_case__  :    Optional[int]    						=      num_attention_heads
          snake_case__  :    Dict    						=      hidden_act
          snake_case__  :    int    						=      hidden_dropout_prob
          snake_case__  :    Optional[Any]    						=      attention_probs_dropout_prob
          snake_case__  :    str    						=      initializer_range
          snake_case__  :    List[str]    						=      drop_path_rate
          snake_case__  :    int    						=      layer_norm_eps
          snake_case__  :    Tuple    						=      decoder_hidden_size
          snake_case__  :    List[Any]    						=      max_depth
          snake_case__  :    Dict    						=      head_in_index
