

"""simple docstring"""




from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class      __lowercase			:
  snake_case_				       =       42  # [batch_size x 3]
  snake_case_				       =       42  # [batch_size x 3]
  snake_case_				       =       42  # [batch_size x 3]
  snake_case_				       =       42  # [batch_size x 3]
  snake_case_				       =       42
  snake_case_				       =       42
  snake_case_				       =       42
  snake_case_				       =       42
  snake_case_				       =       42

  def    __lowercase       (  self		:			Tuple    ):






   '''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		:			Any    ):






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

  def    __lowercase       (  self		:			Tuple    ):






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

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					torch.arange(self.height * self.width    )
   UpperCAmelCase__					:  Union[str, Any]					   =					torch.stack(
       [
           pixel_indices % self.width,
           torch.div(A					,self.width					,rounding_mode="""trunc"""    ),
       ]					,axis=1					,)
   return coords

  @property
  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      *UpperCAmelCase__					:  Any					   =					self.shape
   UpperCAmelCase__					:  List[str]					   =					int(np.prod(A    )    )

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

   UpperCAmelCase__					:  Tuple					   =					rays.view(A					,inner_batch_size * self.height * self.width					,2					,3    )

   return rays

  def    __lowercase       (  self		:			List[str]					,A		:			torch.Tensor    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      *UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					coords.shape
   assert n_coords == 2
   assert batch_size == self.origin.shape[0]

   UpperCAmelCase__					:  Tuple					   =					coords.view(A					,-1					,2    )

   UpperCAmelCase__					:  Dict					   =					self.resolution()
   UpperCAmelCase__					:  List[str]					   =					self.fov()

   UpperCAmelCase__					:  List[str]					   =					(flat.float() / (res - 1)) * 2 - 1
   UpperCAmelCase__					:  Optional[Any]					   =					fracs * torch.tan(fov / 2    )

   UpperCAmelCase__					:  Union[str, Any]					   =					fracs.view(A					,-1					,2    )
   UpperCAmelCase__					:  Optional[int]					   =					(
       self.z.view(A					,1					,3    )
       + self.x.view(A					,1					,3    ) * fracs[:, :, :1]
       + self.y.view(A					,1					,3    ) * fracs[:, :, 1:]
   )
   UpperCAmelCase__					:  Dict					   =					directions / directions.norm(dim=-1					,keepdim=A    )
   UpperCAmelCase__					:  Dict					   =					torch.stack(
       [
           torch.broadcast_to(self.origin.view(A					,1					,3    )					,[batch_size, directions.shape[1], 3]    ),
           directions,
       ]					,dim=2					,)
   return rays.view(A					,*A					,2					,3    )




  def    __lowercase       (  self		:			Union[str, Any]					,A		:			int					,A		:			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=A					,height=A					,x_fov=self.x_fov					,y_fov=self.y_fov					,)







def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  List[str]					   =					[]
 UpperCAmelCase__					:  Dict					   =					[]
 UpperCAmelCase__					:  List[str]					   =					[]
 UpperCAmelCase__					:  Any					   =					[]
 for theta in np.linspace(0							,    2 * np.pi							,    num=20      ):
  UpperCAmelCase__					:  List[str]					   =					np.array([np.sin(__UpperCamelCase      ), np.cos(__UpperCamelCase      ), -0.5]      )
  z /= np.sqrt(np.sum(z**2      )      )
  UpperCAmelCase__					:  List[Any]					   =					-z * 4
  UpperCAmelCase__					:  Optional[int]					   =					np.array([np.cos(__UpperCamelCase      ), -np.sin(__UpperCamelCase      ), 0.0]      )
  UpperCAmelCase__					:  Optional[int]					   =					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      ))							,    )








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase =				{
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TableTransformerForObjectDetection',
          'TableTransformerModel',
          'TableTransformerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_table_transformer import (
          TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          TableTransformerConfig,
          TableTransformerOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_table_transformer import (
                TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TableTransformerForObjectDetection,
                TableTransformerModel,
                TableTransformerPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





