
'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class  __A				:
 lowerCamelCase				       =42  # [batch_size x 3]
 lowerCamelCase				       =42  # [batch_size x 3]
 lowerCamelCase				       =42  # [batch_size x 3]
 lowerCamelCase				       =42  # [batch_size x 3]
 lowerCamelCase				       =42
 lowerCamelCase				       =42
 lowerCamelCase				       =42
 lowerCamelCase				       =42
 lowerCamelCase				       =42
 def 		lowercase_(   self			:    List[str]       ):



   """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			:    int       ):



   """simple docstring"""





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



   """simple docstring"""





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



   """simple docstring"""





   __A		:     Dict												=		torch.arange(self.height * self.width       )
   __A		:     List[Any]												=		torch.stack(
       [
           pixel_indices % self.width,
           torch.div(lowerCamelCase				,   self.width				,   rounding_mode="""trunc"""       ),
       ]				,   axis=1				,   )
   return coords
 @property
 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   __A							,					*__A		:     Dict												=		self.shape
   __A		:     Tuple												=		int(np.prod(lowerCamelCase       )       )

   __A		:     Tuple												=		self.get_image_coords()
   __A		:     List[str]												=		torch.broadcast_to(coords.unsqueeze(0       )				,   [batch_size * inner_batch_size, *coords.shape]       )
   __A		:     Union[str, Any]												=		self.get_camera_rays(lowerCamelCase       )

   __A		:     Union[str, Any]												=		rays.view(lowerCamelCase				,   inner_batch_size * self.height * self.width				,   2				,   3       )

   return rays
 def 		lowercase_(   self			:    Optional[Any]				,   lowerCamelCase			:    torch.Tensor       ):



   """simple docstring"""





   __A							,					*__A							,					__A		:     Optional[int]												=		coords.shape
   assert n_coords == 2
   assert batch_size == self.origin.shape[0]

   __A		:     Any												=		coords.view(lowerCamelCase				,   -1				,   2       )

   __A		:     Optional[Any]												=		self.resolution()
   __A		:     Union[str, Any]												=		self.fov()

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

   __A		:     List[str]												=		fracs.view(lowerCamelCase				,   -1				,   2       )
   __A		:     Any												=		(
       self.z.view(lowerCamelCase				,   1				,   3       )
       + self.x.view(lowerCamelCase				,   1				,   3       ) * fracs[:, :, :1]
       + self.y.view(lowerCamelCase				,   1				,   3       ) * fracs[:, :, 1:]
   )
   __A		:     Tuple												=		directions / directions.norm(dim=-1				,   keepdim=lowerCamelCase       )
   __A		:     Optional[int]												=		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 		lowercase_(   self			:    int				,   lowerCamelCase			:    int				,   lowerCamelCase			:    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=lowerCamelCase				,   height=lowerCamelCase				,   x_fov=self.x_fov				,   y_fov=self.y_fov				,   )







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       DifferentiableProjectiveCamera:





  """simple docstring"""

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





'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__   :		int 		=logging.get_logger(__name__)

A__   :		Union[str, Any] 		={
    'facebook/dpr-ctx_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-single-nq-base': (
        'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-ctx_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-multiset-base': (
        'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'
    ),
}







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''dpr'''
 def __init__(   self			:    Optional[int]				,   lowerCamelCase			:    Tuple=3_05_22				,   lowerCamelCase			:    List[str]=7_68				,   lowerCamelCase			:    Union[str, Any]=12				,   lowerCamelCase			:    Tuple=12				,   lowerCamelCase			:    Dict=30_72				,   lowerCamelCase			:    List[Any]="gelu"				,   lowerCamelCase			:    List[Any]=0.1				,   lowerCamelCase			:    Union[str, Any]=0.1				,   lowerCamelCase			:    Optional[Any]=5_12				,   lowerCamelCase			:    List[Any]=2				,   lowerCamelCase			:    int=0.02				,   lowerCamelCase			:    Union[str, Any]=1e-1_2				,   lowerCamelCase			:    int=0				,   lowerCamelCase			:    List[str]="absolute"				,   lowerCamelCase			:    int = 0				,   **lowerCamelCase			:    List[Any]				,   ):



   """simple docstring"""





   super().__init__(pad_token_id=lowerCamelCase				,   **lowerCamelCase       )

   __A		:     Dict												=		vocab_size
   __A		:     int												=		hidden_size
   __A		:     str												=		num_hidden_layers
   __A		:     Union[str, Any]												=		num_attention_heads
   __A		:     Union[str, Any]												=		hidden_act
   __A		:     Optional[Any]												=		intermediate_size
   __A		:     str												=		hidden_dropout_prob
   __A		:     int												=		attention_probs_dropout_prob
   __A		:     int												=		max_position_embeddings
   __A		:     Dict												=		type_vocab_size
   __A		:     Dict												=		initializer_range
   __A		:     Union[str, Any]												=		layer_norm_eps
   __A		:     int												=		projection_dim
   __A		:     List[Any]												=		position_embedding_type



