from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class 							a				:







 """simple docstring"""






 lowerCamelCase      :Optional[int]            =						42  # [batch_size x 3]
 lowerCamelCase      :Dict            =						42  # [batch_size x 3]
 lowerCamelCase      :Optional[int]            =						42  # [batch_size x 3]
 lowerCamelCase      :Any            =						42  # [batch_size x 3]
 lowerCamelCase      :int            =						42
 lowerCamelCase      :Dict            =						42
 lowerCamelCase      :Union[str, Any]            =						42
 lowerCamelCase      :Dict            =						42
 lowerCamelCase      :List[Any]            =						42



 def 	UpperCAmelCase				(   self				)    ->   Tuple:
        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				)    ->   Any:
        return torch.from_numpy(np.array([self.width, self.height]						,							dtype=np.floataa				)				)



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



 def 	UpperCAmelCase				(   self				)    ->   List[str]:
        _A	  =    torch.arange(self.height * self.width				)
        _A	  =    torch.stack(
            [
                pixel_indices % self.width,
                torch.div(UpperCamelCase_						,							self.width						,							rounding_mode="""trunc"""				),
            ]						,							axis=1						,							)
        return coords



 @property
 def 	UpperCAmelCase				(   self				)    ->   Any:
        _A	  =    self.shape
        _A	  =    int(np.prod(UpperCamelCase_				)				)

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

        _A	  =    rays.view(UpperCamelCase_						,							inner_batch_size * self.height * self.width						,							2						,							3				)

        return rays



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   int:
        _A	  =    coords.shape
        assert n_coords == 2
        assert batch_size == self.origin.shape[0]

        _A	  =    coords.view(UpperCamelCase_						,							-1						,							2				)

        _A	  =    self.resolution()
        _A	  =    self.fov()

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

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








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

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							None
		_a       					=							None
		_a       					=							None
		_a       					=							None



class   lowercase      (				A__   ):

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_=1       ,  UpperCamelCase_=0       ,  UpperCamelCase_=2       ,  UpperCamelCase_=512       ,  UpperCamelCase_="cls"       ,  UpperCamelCase_=False       ,  UpperCamelCase_=True       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


							super().__init__(pad_token_id=UpperCamelCase_       ,  bos_token_id=UpperCamelCase_       ,  eos_token_id=UpperCamelCase_       ,  **UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					project_dim
							UpperCamelCase__  :List[str]				   =					pooler_fn
							UpperCamelCase__  :Dict				   =					learn_encoder
							UpperCamelCase__  :Any				   =					use_attention_mask



class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							[R'pooler', R'logit_scale']
		_a       					=							[R'position_ids', R'predictions.decoder.bias']
		_a       					=							'roberta'
		_a       					=							RobertaSeriesConfig




		def __init__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							super().__init__(UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					XLMRobertaModel(UpperCamelCase_				)
							UpperCamelCase__  :Tuple				   =					nn.Linear(config.hidden_size       ,  config.project_dim				)
							UpperCamelCase__  :Union[str, Any]				   =					getattr(UpperCamelCase_       ,  '''has_pre_transformation'''       ,  UpperCamelCase_				)
							if self.has_pre_transformation:
												UpperCamelCase__  :Any				   =					nn.Linear(config.hidden_size       ,  config.project_dim				)
												UpperCamelCase__  :str				   =					nn.LayerNorm(config.hidden_size       ,  eps=config.layer_norm_eps				)
							self.post_init()




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					return_dict if return_dict is not None else self.config.use_return_dict

							UpperCamelCase__  :str				   =					self.base_model(
							    input_ids=UpperCamelCase_       ,  attention_mask=UpperCamelCase_       ,  token_type_ids=UpperCamelCase_       ,  position_ids=UpperCamelCase_       ,  head_mask=UpperCamelCase_       ,  inputs_embeds=UpperCamelCase_       ,  encoder_hidden_states=UpperCamelCase_       ,  encoder_attention_mask=UpperCamelCase_       ,  output_attentions=UpperCamelCase_       ,  output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,  return_dict=UpperCamelCase_       ,  )

							if self.has_pre_transformation:
												UpperCamelCase__  :Dict				   =					outputs['''hidden_states'''][-2]
												UpperCamelCase__  :Optional[int]				   =					self.pre_LN(UpperCamelCase_				)
												UpperCamelCase__  :Optional[Any]				   =					self.transformation_pre(UpperCamelCase_				)

												return TransformationModelOutput(
												    projection_state=UpperCamelCase_       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )
							else:
												UpperCamelCase__  :List[Any]				   =					self.transformation(outputs.last_hidden_state				)
												return TransformationModelOutput(
												    projection_state=UpperCamelCase_       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )