

'''simple docstring'''


from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




@dataclass
class     a			:






						"""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       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								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       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  torch.arange(self.height * self.width  )
								snake_case__						:       List[Any]       						=  torch.stack(
								    [
								        pixel_indices % self.width,
								        torch.div(snake_case_			,			self.width			,			rounding_mode='''trunc'''  ),
								    ]			,			axis=1			,			)
								return coords







						@property
						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.shape
								snake_case__						:       Dict       						=  int(np.prod(snake_case_  )  )

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

								snake_case__						:       Any       						=  rays.view(snake_case_			,			inner_batch_size * self.height * self.width			,			2			,			3  )

								return rays







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      torch.Tensor  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  coords.shape
								assert n_coords == 2
								assert batch_size == self.origin.shape[0]

								snake_case__						:       Union[str, Any]       						=  coords.view(snake_case_			,			-1			,			2  )

								snake_case__						:       Optional[int]       						=  self.resolution()
								snake_case__						:       List[str]       						=  self.fov()

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

								snake_case__						:       Tuple       						=  fracs.view(snake_case_			,			-1			,			2  )
								snake_case__						:       Optional[int]       						=  (
								    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:]
								)
								snake_case__						:       List[Any]       						=  directions / directions.norm(dim=-1			,			keepdim=snake_case_  )
								snake_case__						:       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       __magic_name__							(							self					:      Dict			,			snake_case_					:      int			,			snake_case_					:      int  ):



								'''simple docstring'''



								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 	_a       (				__lowerCAmelCase		:      str     ):

		"""simple docstring"""

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






'''simple docstring'''


from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
					import timm


if is_torch_available():
					from torch import Tensor




class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    """pixel_values"""
						__UpperCAmelCase			     =    False
						__UpperCAmelCase			     =    TimmBackboneConfig







						def __init__(							self					:      str			,			snake_case_					:      str			,			**snake_case_					:      Dict  ):



								'''simple docstring'''



								requires_backends(self			,			'''timm'''  )
								super().__init__(snake_case_  )
								snake_case__						:       List[str]       						=  config

								if config.backbone is None:
										raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''  )

								if config.backbone not in timm.list_models():
										raise ValueError(F"""backbone {config.backbone} is not supported by timm."""  )

								if hasattr(snake_case_			,			'''out_features'''  ) and config.out_features is not None:
										raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''  )

								snake_case__						:       Dict       						=  getattr(snake_case_			,			'''use_pretrained_backbone'''			,			snake_case_  )
								if pretrained is None:
										raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''  )

								# We just take the final layer by default. This matches the default for the transformers models.
								snake_case__						:       int       						=  config.out_indices if getattr(snake_case_			,			'''out_indices'''			,			snake_case_  ) is not None else (-1,)

								snake_case__						:       Any       						=  timm.create_model(
								    config.backbone			,			pretrained=snake_case_			,			features_only=config.features_only			,			in_chans=config.num_channels			,			out_indices=snake_case_			,			**snake_case_			,			)
								# These are used to control the output of the model when called. If output_hidden_states is True, then
								# return_layers is modified to include all layers.
								snake_case__						:       str       						=  self._backbone.return_layers
								snake_case__						:       List[Any]       						=  {layer['''module''']: str(snake_case_  ) for i, layer in enumerate(self._backbone.feature_info.info  )}
								super()._init_backbone(snake_case_  )







						@classmethod
						def       __magic_name__							(							cls					:      Union[str, Any]			,			snake_case_					:      Union[str, Any]			,			*snake_case_					:      Optional[Any]			,			**snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								requires_backends(cls			,			['''vision''', '''timm''']  )
								from ...models.timm_backbone import TimmBackboneConfig

								snake_case__						:       Dict       						=  kwargs.pop('''config'''			,			TimmBackboneConfig()  )

								snake_case__						:       Optional[Any]       						=  kwargs.pop('''use_timm_backbone'''			,			snake_case_  )
								if not use_timm:
										raise ValueError('''use_timm_backbone must be True for timm backbones'''  )

								snake_case__						:       List[Any]       						=  kwargs.pop('''num_channels'''			,			config.num_channels  )
								snake_case__						:       Optional[Any]       						=  kwargs.pop('''features_only'''			,			config.features_only  )
								snake_case__						:       Tuple       						=  kwargs.pop('''use_pretrained_backbone'''			,			config.use_pretrained_backbone  )
								snake_case__						:       Union[str, Any]       						=  kwargs.pop('''out_indices'''			,			config.out_indices  )
								snake_case__						:       int       						=  TimmBackboneConfig(
								    backbone=snake_case_			,			num_channels=snake_case_			,			features_only=snake_case_			,			use_pretrained_backbone=snake_case_			,			out_indices=snake_case_			,			)
								return super()._from_config(snake_case_			,			**snake_case_  )







						def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      Union[str, Any]  ):



								'''simple docstring'''



								pass







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Tuple=None			,			snake_case_					:      Tuple=None			,			snake_case_					:      Optional[Any]=None			,			**snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  return_dict if return_dict is not None else self.config.use_return_dict
								snake_case__						:       Optional[int]       						=  (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								snake_case__						:       int       						=  output_attentions if output_attentions is not None else self.config.output_attentions

								if output_attentions:
										raise ValueError('''Cannot output attentions for timm backbones at the moment'''  )

								if output_hidden_states:
										# We modify the return layers to include all the stages of the backbone
										snake_case__						:       Any       						=  self._all_layers
										snake_case__						:       str       						=  self._backbone(snake_case_			,			**snake_case_  )
										snake_case__						:       Tuple       						=  self._return_layers
										snake_case__						:       str       						=  tuple(hidden_states[i] for i in self.out_indices  )
								else:
										snake_case__						:       Dict       						=  self._backbone(snake_case_			,			**snake_case_  )
										snake_case__						:       Any       						=  None

								snake_case__						:       Union[str, Any]       						=  tuple(snake_case_  )
								snake_case__						:       Optional[int]       						=  tuple(snake_case_  ) if hidden_states is not None else None

								if not return_dict:
										snake_case__						:       List[str]       						=  (feature_maps,)
										if output_hidden_states:
												snake_case__						:       Optional[int]       						=  output + (hidden_states,)
										return output

								return BackboneOutput(feature_maps=snake_case_			,			hidden_states=snake_case_			,			attentions=snake_case_  )



