






"""simple docstring"""
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




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





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





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





	def    lowerCamelCase(self							):
		A_		:						int	    =   torch.arange(self.height * self.width							)
		A_		:						Optional[int]	    =   torch.stack(
		    [
		        pixel_indices % self.width,
		        torch.div(lowerCAmelCase__     ,     self.width     ,     rounding_mode="""trunc"""							),
		    ]     ,     axis=1     ,     )
		return coords





	@property
	def    lowerCamelCase(self							):
		A_		:						Dict	    =   self.shape
		A_		:						Any	    =   int(np.prod(lowerCAmelCase__							)							)

		A_		:						Optional[int]	    =   self.get_image_coords()
		A_		:						List[str]	    =   torch.broadcast_to(coords.unsqueeze(0							)     ,     [batch_size * inner_batch_size, *coords.shape]							)
		A_		:						Dict	    =   self.get_camera_rays(lowerCAmelCase__							)

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

		return rays





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						List[Any]	    =   coords.shape
		assert n_coords == 2
		assert batch_size == self.origin.shape[0]

		A_		:						Optional[int]	    =   coords.view(lowerCAmelCase__     ,     -1     ,     2							)

		A_		:						Dict	    =   self.resolution()
		A_		:						Optional[Any]	    =   self.fov()

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

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





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		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=lowerCAmelCase__     ,     height=lowerCAmelCase__     ,     x_fov=self.x_fov     ,     y_fov=self.y_fov     ,     )






def 			__UpperCamelCase	(		snake_case__  ):
	A_		:						int	    =   []
	A_		:						str	    =   []
	A_		:						List[str]	    =   []
	A_		:						Optional[Any]	    =   []
	for theta in np.linspace(0   ,   2 * np.pi   ,   num=20  ):
		A_		:						Optional[Any]	    =   np.array([np.sin(_lowercase  ), np.cos(_lowercase  ), -0.5]  )
		z /= np.sqrt(np.sum(z**2  )  )
		A_		:						Union[str, Any]	    =   -z * 4
		A_		:						Tuple	    =   np.array([np.cos(_lowercase  ), -np.sin(_lowercase  ), 0.0]  )
		A_		:						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  ))   ,   )









"""simple docstring"""
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				List[str]    =				"""deta"""
	_A       :				Tuple    =				{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}





	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=900     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=1024     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_="relu"     ,     lowerCAmelCase_=256     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=1.0     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_="sine"     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=300     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.25     ,     **lowerCAmelCase_     ,     ):
		if backbone_config is None:
			logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
			A_		:						Any	    =   CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]							)
		else:
			if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							):
				A_		:						List[str]	    =   backbone_config.pop("""model_type"""							)
				A_		:						List[str]	    =   CONFIG_MAPPING[backbone_model_type]
				A_		:						Tuple	    =   config_class.from_dict(lowerCAmelCase_							)

		A_		:						List[Any]	    =   backbone_config
		A_		:						List[str]	    =   num_queries
		A_		:						List[Any]	    =   max_position_embeddings
		A_		:						int	    =   d_model
		A_		:						Optional[Any]	    =   encoder_ffn_dim
		A_		:						Optional[Any]	    =   encoder_layers
		A_		:						List[Any]	    =   encoder_attention_heads
		A_		:						int	    =   decoder_ffn_dim
		A_		:						Dict	    =   decoder_layers
		A_		:						Dict	    =   decoder_attention_heads
		A_		:						Optional[Any]	    =   dropout
		A_		:						str	    =   attention_dropout
		A_		:						Any	    =   activation_dropout
		A_		:						Union[str, Any]	    =   activation_function
		A_		:						int	    =   init_std
		A_		:						List[Any]	    =   init_xavier_std
		A_		:						List[Any]	    =   encoder_layerdrop
		A_		:						Any	    =   auxiliary_loss
		A_		:						Tuple	    =   position_embedding_type
		# deformable attributes
		A_		:						List[str]	    =   num_feature_levels
		A_		:						Tuple	    =   encoder_n_points
		A_		:						str	    =   decoder_n_points
		A_		:						List[Any]	    =   two_stage
		A_		:						Optional[Any]	    =   two_stage_num_proposals
		A_		:						Union[str, Any]	    =   with_box_refine
		A_		:						Optional[int]	    =   assign_first_stage
		if two_stage is True and with_box_refine is False:
			raise ValueError("""If two_stage is True, with_box_refine must be True."""							)
		# Hungarian matcher
		A_		:						List[Any]	    =   class_cost
		A_		:						str	    =   bbox_cost
		A_		:						Tuple	    =   giou_cost
		# Loss coefficients
		A_		:						int	    =   mask_loss_coefficient
		A_		:						Union[str, Any]	    =   dice_loss_coefficient
		A_		:						Union[str, Any]	    =   bbox_loss_coefficient
		A_		:						Optional[Any]	    =   giou_loss_coefficient
		A_		:						List[str]	    =   eos_coefficient
		A_		:						Optional[Any]	    =   focal_alpha
		super().__init__(is_encoder_decoder=lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		return self.encoder_attention_heads





	@property
	def    lowerCamelCase(self							):
		return self.d_model





	def    lowerCamelCase(self							):
		A_		:						str	    =   copy.deepcopy(self.__dict__							)
		A_		:						int	    =   self.backbone_config.to_dict()
		A_		:						Dict	    =   self.__class__.model_type
		return output

