



'''simple docstring'''






from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class 							lowercase       :


 """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 _snake_case (							self							)		->   List[str]:
        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 _snake_case (							self							)		->   Dict:
        return torch.from_numpy(np.array([self.width, self.height]  ,dtype=np.floataa							)							)







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







 def _snake_case (							self							)		->   torch.Tensor:
        _UpperCAmelCase  :						int			     = torch.arange(self.height * self.width							)
        _UpperCAmelCase  :						List[str]			     = torch.stack(
            [
                pixel_indices % self.width,
                torch.div(a_  ,self.width  ,rounding_mode="""trunc"""							),
            ]  ,axis=1  ,)
        return coords







 @property
 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase     ,*_UpperCAmelCase  :						Optional[Any]			     = self.shape
        _UpperCAmelCase  :						List[Any]			     = int(np.prod(a_							)							)

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

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

        return rays







 def _snake_case (							self  ,a_							)		->   torch.Tensor:
        _UpperCAmelCase     ,*_UpperCAmelCase     ,_UpperCAmelCase  :						Any			     = coords.shape
        assert n_coords == 2
        assert batch_size == self.origin.shape[0]

        _UpperCAmelCase  :						List[str]			     = coords.view(a_  ,-1  ,2							)

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

        _UpperCAmelCase  :						Tuple			     = (flat.float() / (res - 1)) * 2 - 1
        _UpperCAmelCase  :						str			     = fracs * torch.tan(fov / 2							)

        _UpperCAmelCase  :						Union[str, Any]			     = fracs.view(a_  ,-1  ,2							)
        _UpperCAmelCase  :						str			     = (
            self.z.view(a_  ,1  ,3							)
            + self.x.view(a_  ,1  ,3							) * fracs[:, :, :1]
            + self.y.view(a_  ,1  ,3							) * fracs[:, :, 1:]
        )
        _UpperCAmelCase  :						Union[str, Any]			     = directions / directions.norm(dim=-1  ,keepdim=a_							)
        _UpperCAmelCase  :						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 _snake_case (							self  ,a_  ,a_							)		->   "DifferentiableProjectiveCamera":
        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     snake_case_					(     lowerCAmelCase_		)->		DifferentiableProjectiveCamera:





       '''simple docstring'''


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








'''simple docstring'''






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


A_							: Any						 =	logging.get_logger(__name__)

A_							: Any						 =	{
    """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 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """markuplm"""







 def __init__(							self  ,a_=30_522  ,a_=768  ,a_=12  ,a_=12  ,a_=3_072  ,a_="gelu"  ,a_=0.1  ,a_=0.1  ,a_=512  ,a_=2  ,a_=0.02  ,a_=1E-1_2  ,a_=0  ,a_=0  ,a_=2  ,a_=256  ,a_=1_024  ,a_=216  ,a_=1_001  ,a_=32  ,a_=50  ,a_="absolute"  ,a_=True  ,a_=None  ,**a_  ,)		->   Union[str, Any]:
        super().__init__(
            pad_token_id=a_  ,bos_token_id=a_  ,eos_token_id=a_  ,**a_  ,)
        _UpperCAmelCase  :						Optional[int]			     = vocab_size
        _UpperCAmelCase  :						Tuple			     = hidden_size
        _UpperCAmelCase  :						str			     = num_hidden_layers
        _UpperCAmelCase  :						Dict			     = num_attention_heads
        _UpperCAmelCase  :						int			     = hidden_act
        _UpperCAmelCase  :						Optional[Any]			     = intermediate_size
        _UpperCAmelCase  :						Tuple			     = hidden_dropout_prob
        _UpperCAmelCase  :						List[str]			     = attention_probs_dropout_prob
        _UpperCAmelCase  :						Optional[int]			     = max_position_embeddings
        _UpperCAmelCase  :						Tuple			     = type_vocab_size
        _UpperCAmelCase  :						Dict			     = initializer_range
        _UpperCAmelCase  :						List[Any]			     = layer_norm_eps
        _UpperCAmelCase  :						Optional[Any]			     = position_embedding_type
        _UpperCAmelCase  :						Any			     = use_cache
        _UpperCAmelCase  :						List[Any]			     = classifier_dropout
        # additional properties
        _UpperCAmelCase  :						Dict			     = max_depth
        _UpperCAmelCase  :						Union[str, Any]			     = max_xpath_tag_unit_embeddings
        _UpperCAmelCase  :						Optional[int]			     = max_xpath_subs_unit_embeddings
        _UpperCAmelCase  :						List[Any]			     = tag_pad_id
        _UpperCAmelCase  :						Tuple			     = subs_pad_id
        _UpperCAmelCase  :						List[str]			     = xpath_unit_hidden_size



