




from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class     A:



 '''simple docstring'''


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

 def 							a__					(							self   :    Optional[int]    )  ->      List[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   :    Union[str, Any]    )  ->      List[Any]:

       """simple docstring"""






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

 def 							a__					(							self   :    Dict    )  ->      Union[str, Any]:

       """simple docstring"""






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

 def 							a__					(							self   :    List[Any]    )  ->      torch.Tensor:

       """simple docstring"""






       lowerCamelCase_							=       torch.arange(self.height * self.width    )
       lowerCamelCase_							=       torch.stack(
           [
               pixel_indices % self.width,
               torch.div(A_		,				self.width		,				rounding_mode='trunc'    ),
           ]		,				axis=1		,				)
       return coords

 @property
 def 							a__					(							self   :    List[Any]    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_	,							*lowerCamelCase_							=       self.shape
       lowerCamelCase_							=       int(np.prod(A_    )    )

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

       lowerCamelCase_							=       rays.view(A_		,				inner_batch_size * self.height * self.width		,				2		,				3    )

       return rays

 def 							a__					(							self   :    int		,				A_   :    torch.Tensor    )  ->      torch.Tensor:

       """simple docstring"""






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

       lowerCamelCase_							=       coords.view(A_		,				-1		,				2    )

       lowerCamelCase_							=       self.resolution()
       lowerCamelCase_							=       self.fov()

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

       lowerCamelCase_							=       fracs.view(A_		,				-1		,				2    )
       lowerCamelCase_							=       (
           self.z.view(A_		,				1		,				3    )
           + self.x.view(A_		,				1		,				3    ) * fracs[:, :, :1]
           + self.y.view(A_		,				1		,				3    ) * fracs[:, :, 1:]
       )
       lowerCamelCase_							=       directions / directions.norm(dim=-1		,				keepdim=A_    )
       lowerCamelCase_							=       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 							a__					(							self   :    Any		,				A_   :    int		,				A_   :    int    )  ->      "DifferentiableProjectiveCamera":

       """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      _SCREAMING_SNAKE_CASE     (				lowercase      :			int	):


      '''simple docstring'''



      lowerCamelCase_							=       []
      lowerCamelCase_							=       []
      lowerCamelCase_							=       []
      lowerCamelCase_							=       []
      for theta in np.linspace(0      ,				2 * np.pi      ,				num=20	):
            lowerCamelCase_							=       np.array([np.sin(lowercase	), np.cos(lowercase	), -0.5]	)
            z /= np.sqrt(np.sum(z**2	)	)
            lowerCamelCase_							=       -z * 4
            lowerCamelCase_							=       np.array([np.cos(lowercase	), -np.sin(lowercase	), 0.0]	)
            lowerCamelCase_							=       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	))      ,				)





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCamelCase__     :		Union[str, Any]        = logging.get_logger(__name__)



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Any	   =      ['pixel_values']


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 1 / 2_55       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 8       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   do_rescale
   lowercase__	: List[Any]     		=   rescale_factor
   lowercase__	: Tuple     		=   do_pad
   lowercase__	: Optional[Any]     		=   pad_size


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return rescale(SCREAMING_SNAKE_CASE_       ,	scale=SCREAMING_SNAKE_CASE_       ,	data_format=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__		,     lowercase__	: Optional[int]     		=   get_image_size(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   (old_height // size + 1) * size - old_height
   lowercase__	: str     		=   (old_width // size + 1) * size - old_width

   return pad(SCREAMING_SNAKE_CASE_       ,	((0, pad_height), (0, pad_width))       ,	mode="""symmetric"""       ,	data_format=SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   do_rescale if do_rescale is not None else self.do_rescale
   lowercase__	: int     		=   rescale_factor if rescale_factor is not None else self.rescale_factor
   lowercase__	: Union[str, Any]     		=   do_pad if do_pad is not None else self.do_pad
   lowercase__	: Optional[Any]     		=   pad_size if pad_size is not None else self.pad_size

   lowercase__	: str     		=   make_list_of_images(SCREAMING_SNAKE_CASE_)

   if not valid_images(SCREAMING_SNAKE_CASE_):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray.""")

   if do_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

   # All transformations expect numpy arrays.
   lowercase__	: List[Any]     		=   [to_numpy_array(SCREAMING_SNAKE_CASE_) for image in images]

   if do_rescale:
     lowercase__	: str     		=   [self.rescale(image=SCREAMING_SNAKE_CASE_       ,	scale=SCREAMING_SNAKE_CASE_) for image in images]

   if do_pad:
     lowercase__	: List[str]     		=   [self.pad(SCREAMING_SNAKE_CASE_       ,	size=SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Optional[Any]     		=   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Dict     		=   {"""pixel_values""": images}
   return BatchFeature(data=SCREAMING_SNAKE_CASE_       ,	tensor_type=SCREAMING_SNAKE_CASE_)






