




"""simple docstring"""






from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




@dataclass
class   a__			:
						snake_case__             = 4_2  # [batch_size x 3]
						snake_case__             = 4_2  # [batch_size x 3]
						snake_case__             = 4_2  # [batch_size x 3]
						snake_case__             = 4_2  # [batch_size x 3]
						snake_case__             = 4_2
						snake_case__             = 4_2
						snake_case__             = 4_2
						snake_case__             = 4_2
						snake_case__             = 4_2





						def       __UpperCamelCase     (							self      :	Optional[int])						->       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       __UpperCamelCase     (							self      :	int)						->       Any:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	Any)						->       List[str]:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	int)						->       torch.Tensor:




											"""simple docstring"""







											_lowerCAmelCase:int									=  torch.arange(self.height * self.width)
											_lowerCAmelCase:str									=  torch.stack(
											    [
											        pixel_indices % self.width,
											        torch.div(a__     ,self.width     ,rounding_mode='''trunc'''),
											    ]     ,axis=1     ,)
											return coords





						@property
						def       __UpperCamelCase     (							self      :	int)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.shape
											_lowerCAmelCase:List[Any]									=  int(np.prod(a__))

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

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

											return rays





						def       __UpperCamelCase     (							self      :	Dict     ,a__      :	torch.Tensor)						->       torch.Tensor:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  coords.shape
											assert n_coords == 2
											assert batch_size == self.origin.shape[0]

											_lowerCAmelCase:int									=  coords.view(a__     ,-1     ,2)

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

											_lowerCAmelCase:Tuple									=  (flat.float() / (res - 1)) * 2 - 1
											_lowerCAmelCase:Union[str, Any]									=  fracs * torch.tan(fov / 2)

											_lowerCAmelCase:str									=  fracs.view(a__     ,-1     ,2)
											_lowerCAmelCase:Tuple									=  (
											    self.z.view(a__     ,1     ,3)
											    + self.x.view(a__     ,1     ,3) * fracs[:, :, :1]
											    + self.y.view(a__     ,1     ,3) * fracs[:, :, 1:]
											)
											_lowerCAmelCase:str									=  directions / directions.norm(dim=-1     ,keepdim=a__)
											_lowerCAmelCase:List[str]									=  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       __UpperCamelCase     (							self      :	int     ,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 							UpperCAmelCase		(		snake_case			:					int    ):
					_lowerCAmelCase:Dict									=  []
					_lowerCAmelCase:int									=  []
					_lowerCAmelCase:Union[str, Any]									=  []
					_lowerCAmelCase:Union[str, Any]									=  []
					for theta in np.linspace(0      ,       2 * np.pi      ,       num=20    ):
										_lowerCAmelCase:int									=  np.array([np.sin(snake_case    ), np.cos(snake_case    ), -0.5]    )
										z /= np.sqrt(np.sum(z**2    )    )
										_lowerCAmelCase:Union[str, Any]									=  -z * 4
										_lowerCAmelCase:List[str]									=  np.array([np.cos(snake_case    ), -np.sin(snake_case    ), 0.0]    )
										_lowerCAmelCase:int									=  np.cross(snake_case      ,       snake_case    )
										origins.append(snake_case    )
										xs.append(snake_case    )
										ys.append(snake_case    )
										zs.append(snake_case    )
					return DifferentiableProjectiveCamera(
					    origin=torch.from_numpy(np.stack(snake_case      ,       axis=0    )    ).float()      ,       x=torch.from_numpy(np.stack(snake_case      ,       axis=0    )    ).float()      ,       y=torch.from_numpy(np.stack(snake_case      ,       axis=0    )    ).float()      ,       z=torch.from_numpy(np.stack(snake_case      ,       axis=0    )    ).float()      ,       width=snake_case      ,       height=snake_case      ,       x_fov=0.7      ,       y_fov=0.7      ,       shape=(1, len(snake_case    ))      ,       )












"""simple docstring"""






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


UpperCamelCase__						=			logging.get_logger(__name__)

UpperCamelCase__						=			{
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}




class   a__			(       UpperCamelCase_  ):
						snake_case__             = '''roc_bert'''





						def __init__(							self      :	Union[str, Any]     ,a__      :	Union[str, Any]=3_0522     ,a__      :	List[Any]=768     ,a__      :	Tuple=12     ,a__      :	Optional[int]=12     ,a__      :	Any=3072     ,a__      :	Optional[int]="gelu"     ,a__      :	Union[str, Any]=0.1     ,a__      :	List[str]=0.1     ,a__      :	Dict=512     ,a__      :	int=2     ,a__      :	Dict=0.02     ,a__      :	Dict=1E-12     ,a__      :	int=True     ,a__      :	Optional[int]=0     ,a__      :	Union[str, Any]="absolute"     ,a__      :	List[Any]=None     ,a__      :	str=True     ,a__      :	str=True     ,a__      :	List[str]=768     ,a__      :	Optional[int]=910     ,a__      :	Any=512     ,a__      :	str=2_4858     ,a__      :	List[str]=True     ,**a__      :	str     ,)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:Tuple									=  vocab_size
											_lowerCAmelCase:Any									=  max_position_embeddings
											_lowerCAmelCase:Union[str, Any]									=  hidden_size
											_lowerCAmelCase:Optional[Any]									=  num_hidden_layers
											_lowerCAmelCase:int									=  num_attention_heads
											_lowerCAmelCase:int									=  intermediate_size
											_lowerCAmelCase:Union[str, Any]									=  hidden_act
											_lowerCAmelCase:Any									=  hidden_dropout_prob
											_lowerCAmelCase:List[Any]									=  attention_probs_dropout_prob
											_lowerCAmelCase:List[Any]									=  initializer_range
											_lowerCAmelCase:Dict									=  type_vocab_size
											_lowerCAmelCase:Dict									=  layer_norm_eps
											_lowerCAmelCase:str									=  use_cache
											_lowerCAmelCase:Any									=  enable_pronunciation
											_lowerCAmelCase:List[str]									=  enable_shape
											_lowerCAmelCase:Optional[int]									=  pronunciation_embed_dim
											_lowerCAmelCase:Union[str, Any]									=  pronunciation_vocab_size
											_lowerCAmelCase:str									=  shape_embed_dim
											_lowerCAmelCase:List[Any]									=  shape_vocab_size
											_lowerCAmelCase:str									=  concat_input
											_lowerCAmelCase:Optional[int]									=  position_embedding_type
											_lowerCAmelCase:Any									=  classifier_dropout
											super().__init__(pad_token_id=a__     ,**a__)






