

'''simple docstring'''





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class 	lowercase    :


		"""simple docstring"""




		a__					       =  4_2  # [batch_size x 3]
		a__					       =  4_2  # [batch_size x 3]
		a__					       =  4_2  # [batch_size x 3]
		a__					       =  4_2  # [batch_size x 3]
		a__					       =  4_2
		a__					       =  4_2
		a__					       =  4_2
		a__					       =  4_2
		a__					       =  4_2


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


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


		def 		A__					(				self):
			_UpperCamelCase   :		Optional[int]			=						torch.arange(self.height * self.width)
			_UpperCamelCase   :		Optional[Any]			=						torch.stack(
			    [
			        pixel_indices % self.width,
			        torch.div(__snake_case	,       self.width	,       rounding_mode='trunc'),
			    ]	,       axis=1	,       )
			return coords


		@property
		def 		A__					(				self):
			_UpperCamelCase   :		str			=						self.shape
			_UpperCamelCase   :		Any			=						int(np.prod(__snake_case))

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

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

			return rays


		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		Optional[Any]			=						coords.shape
			assert n_coords == 2
			assert batch_size == self.origin.shape[0]

			_UpperCamelCase   :		str			=						coords.view(__snake_case	,       -1	,       2)

			_UpperCamelCase   :		Optional[Any]			=						self.resolution()
			_UpperCamelCase   :		str			=						self.fov()

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

			_UpperCamelCase   :		Optional[Any]			=						fracs.view(__snake_case	,       -1	,       2)
			_UpperCamelCase   :		Any			=						(
			    self.z.view(__snake_case	,       1	,       3)
			    + self.x.view(__snake_case	,       1	,       3) * fracs[:, :, :1]
			    + self.y.view(__snake_case	,       1	,       3) * fracs[:, :, 1:]
			)
			_UpperCamelCase   :		int			=						directions / directions.norm(dim=-1	,       keepdim=__snake_case)
			_UpperCamelCase   :		Union[str, Any]			=						torch.stack(
			    [
			        torch.broadcast_to(self.origin.view(__snake_case	,       1	,       3)	,       [batch_size, directions.shape[1], 3]),
			        directions,
			    ]	,       dim=2	,       )
			return rays.view(__snake_case	,       *__snake_case	,       2	,       3)






		def 		A__					(				self	,       __snake_case	,       __snake_case):
			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=__snake_case	,       height=__snake_case	,       x_fov=self.x_fov	,       y_fov=self.y_fov	,       )






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int	)						->				DifferentiableProjectiveCamera:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						[]
	_UpperCamelCase   :		Optional[Any]			=						[]
	_UpperCamelCase   :		Tuple			=						[]
	_UpperCamelCase   :		Tuple			=						[]
	for theta in np.linspace(0							,       2 * np.pi							,       num=2_0	):
		_UpperCamelCase   :		Optional[Any]			=						np.array([np.sin(UpperCAmelCase_	), np.cos(UpperCAmelCase_	), -0.5]	)
		z /= np.sqrt(np.sum(z**2	)	)
		_UpperCamelCase   :		List[Any]			=						-z * 4
		_UpperCamelCase   :		Dict			=						np.array([np.cos(UpperCAmelCase_	), -np.sin(UpperCAmelCase_	), 0.0]	)
		_UpperCamelCase   :		int			=						np.cross(UpperCAmelCase_							,       UpperCAmelCase_	)
		origins.append(UpperCAmelCase_	)
		xs.append(UpperCAmelCase_	)
		ys.append(UpperCAmelCase_	)
		zs.append(UpperCAmelCase_	)
	return DifferentiableProjectiveCamera(
	    origin=torch.from_numpy(np.stack(UpperCAmelCase_							,       axis=0	)	).float()							,       x=torch.from_numpy(np.stack(UpperCAmelCase_							,       axis=0	)	).float()							,       y=torch.from_numpy(np.stack(UpperCAmelCase_							,       axis=0	)	).float()							,       z=torch.from_numpy(np.stack(UpperCAmelCase_							,       axis=0	)	).float()							,       width=UpperCAmelCase_							,       height=UpperCAmelCase_							,       x_fov=0.7							,       y_fov=0.7							,       shape=(1, len(UpperCAmelCase_	))							,       )







import logging

from transformers.configuration_utils import PretrainedConfig


lowerCAmelCase__								=		logging.getLogger(__name__)







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "masked_bert"


		def __init__(				self	,       __snake_case=3_05_22	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=0	,       __snake_case="topK"	,       __snake_case="constant"	,       __snake_case=0.0	,       **__snake_case	,       ):
			super().__init__(pad_token_id=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		List[Any]			=						vocab_size
			_UpperCamelCase   :		Union[str, Any]			=						hidden_size
			_UpperCamelCase   :		Optional[int]			=						num_hidden_layers
			_UpperCamelCase   :		Any			=						num_attention_heads
			_UpperCamelCase   :		int			=						hidden_act
			_UpperCamelCase   :		str			=						intermediate_size
			_UpperCamelCase   :		str			=						hidden_dropout_prob
			_UpperCamelCase   :		Any			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Tuple			=						max_position_embeddings
			_UpperCamelCase   :		Dict			=						type_vocab_size
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		List[Any]			=						layer_norm_eps
			_UpperCamelCase   :		Tuple			=						pruning_method
			_UpperCamelCase   :		Tuple			=						mask_init
			_UpperCamelCase   :		Dict			=						mask_scale




