






from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch


@dataclass
class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     UpperCamelCase_							:		torch.Tensor  # [batch_size x 3]
     UpperCamelCase_							:		torch.Tensor  # [batch_size x 3]
     UpperCamelCase_							:		torch.Tensor  # [batch_size x 3]
     UpperCamelCase_							:		torch.Tensor  # [batch_size x 3]
     UpperCamelCase_							:		int
     UpperCamelCase_							:		int
     UpperCamelCase_							:		float
     UpperCamelCase_							:		float
     UpperCamelCase_							:		Tuple[int]


     def 				_A       (				self       :	Any      ):
       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       :	List[str]      ):
       return torch.from_numpy(np.array([self.width, self.height]					, dtype=np.floataa      )      )


     def 				_A       (				self       :	Optional[int]      ):
       return torch.from_numpy(np.array([self.x_fov, self.y_fov]					, dtype=np.floataa      )      )


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    torch.arange(self.height * self.width      )
       SCREAMING_SNAKE_CASE       :      Dict							=    torch.stack(
           [
               pixel_indices % self.width,
               torch.div(UpperCAmelCase_					, self.width					, rounding_mode="trunc"      ),
           ]					, axis=1					, )
       return coords


     @property
     def 				_A       (				self       :	int      ):
       SCREAMING_SNAKE_CASE						,  *SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.shape
       SCREAMING_SNAKE_CASE       :      Any							=    int(np.prod(UpperCAmelCase_      )      )

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

       SCREAMING_SNAKE_CASE       :      int							=    rays.view(UpperCAmelCase_					, inner_batch_size * self.height * self.width					, 2					, 3      )

       return rays


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	torch.Tensor      ):
       SCREAMING_SNAKE_CASE						,  *SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Optional[int]							=    coords.shape
       assert n_coords == 2
       assert batch_size == self.origin.shape[0]

       SCREAMING_SNAKE_CASE       :      Any							=    coords.view(UpperCAmelCase_					, -1					, 2      )

       SCREAMING_SNAKE_CASE       :      Dict							=    self.resolution()
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.fov()

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

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






     def 				_A       (				self       :	int					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	int      ):
       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=UpperCAmelCase_					, height=UpperCAmelCase_					, x_fov=self.x_fov					, y_fov=self.y_fov					, )





def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    []
  SCREAMING_SNAKE_CASE       :      Any							=    []
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    []
  SCREAMING_SNAKE_CASE       :      str							=    []
  for theta in np.linspace(0						,     2 * np.pi						,     num=20				):
    SCREAMING_SNAKE_CASE       :      List[Any]							=    np.array([np.sin(lowercase				), np.cos(lowercase				), -0.5]				)
    z /= np.sqrt(np.sum(z**2				)				)
    SCREAMING_SNAKE_CASE       :      Dict							=    -z * 4
    SCREAMING_SNAKE_CASE       :      Any							=    np.array([np.cos(lowercase				), -np.sin(lowercase				), 0.0]				)
    SCREAMING_SNAKE_CASE       :      List[Any]							=    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 ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[int]			=	'''markuplm'''


     def __init__(				self       :	int					, UpperCAmelCase_       :	List[str]=3_0522					, UpperCAmelCase_       :	Optional[Any]=768					, UpperCAmelCase_       :	Any=12					, UpperCAmelCase_       :	Tuple=12					, UpperCAmelCase_       :	Union[str, Any]=3072					, UpperCAmelCase_       :	Dict="gelu"					, UpperCAmelCase_       :	Optional[int]=0.1					, UpperCAmelCase_       :	Tuple=0.1					, UpperCAmelCase_       :	Dict=512					, UpperCAmelCase_       :	Optional[int]=2					, UpperCAmelCase_       :	Any=0.02					, UpperCAmelCase_       :	Dict=1E-12					, UpperCAmelCase_       :	int=0					, UpperCAmelCase_       :	Optional[Any]=0					, UpperCAmelCase_       :	Optional[int]=2					, UpperCAmelCase_       :	str=256					, UpperCAmelCase_       :	str=1024					, UpperCAmelCase_       :	List[str]=216					, UpperCAmelCase_       :	List[Any]=1001					, UpperCAmelCase_       :	Union[str, Any]=32					, UpperCAmelCase_       :	Dict=50					, UpperCAmelCase_       :	Optional[int]="absolute"					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Any=None					, **UpperCAmelCase_       :	Any					, ):
       super().__init__(
           pad_token_id=UpperCAmelCase_					, bos_token_id=UpperCAmelCase_					, eos_token_id=UpperCAmelCase_					, **UpperCAmelCase_					, )
       SCREAMING_SNAKE_CASE       :      Any							=    vocab_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    hidden_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Tuple							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      List[str]							=    hidden_act
       SCREAMING_SNAKE_CASE       :      Tuple							=    intermediate_size
       SCREAMING_SNAKE_CASE       :      Any							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      int							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      Tuple							=    type_vocab_size
       SCREAMING_SNAKE_CASE       :      Any							=    initializer_range
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    layer_norm_eps
       SCREAMING_SNAKE_CASE       :      int							=    position_embedding_type
       SCREAMING_SNAKE_CASE       :      List[str]							=    use_cache
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    classifier_dropout
       # additional properties
       SCREAMING_SNAKE_CASE       :      Any							=    max_depth
       SCREAMING_SNAKE_CASE       :      int							=    max_xpath_tag_unit_embeddings
       SCREAMING_SNAKE_CASE       :      List[str]							=    max_xpath_subs_unit_embeddings
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    tag_pad_id
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    subs_pad_id
       SCREAMING_SNAKE_CASE       :      List[Any]							=    xpath_unit_hidden_size






