
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch


@dataclass
class  __magic_name__						:
	_UpperCAmelCase	:			Optional[int]		   =     42  # [batch_size x 3]
	_UpperCAmelCase	:			List[str]		   =     42  # [batch_size x 3]
	_UpperCAmelCase	:			List[Any]		   =     42  # [batch_size x 3]
	_UpperCAmelCase	:			str		   =     42  # [batch_size x 3]
	_UpperCAmelCase	:			Dict		   =     42
	_UpperCAmelCase	:			Any		   =     42
	_UpperCAmelCase	:			str		   =     42
	_UpperCAmelCase	:			List[str]		   =     42
	_UpperCAmelCase	:			Dict		   =     42





	def 	_UpperCAmelCase						(		self	: Dict       ):
						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	: List[str]       ):
						return torch.from_numpy(np.array([self.width, self.height]    ,dtype=np.floataa       )       )





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





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





	@property
	def 	_UpperCAmelCase						(		self	: Tuple       ):
						UpperCAmelCase   ,     *UpperCAmelCase				   =  self.shape
						UpperCAmelCase				   =  int(np.prod(A_       )       )

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

						UpperCAmelCase				   =  rays.view(A_    ,inner_batch_size * self.height * self.width    ,2    ,3       )

						return rays





	def 	_UpperCAmelCase						(		self	: Any    ,__SCREAMING_SNAKE_CASE	: Dict       ):
						UpperCAmelCase   ,     *UpperCAmelCase   ,     UpperCAmelCase				   =  coords.shape
						assert n_coords == 2
						assert batch_size == self.origin.shape[0]

						UpperCAmelCase				   =  coords.view(A_    ,-1    ,2       )

						UpperCAmelCase				   =  self.resolution()
						UpperCAmelCase				   =  self.fov()

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

						UpperCAmelCase				   =  fracs.view(A_    ,-1    ,2       )
						UpperCAmelCase				   =  (
						    self.z.view(A_    ,1    ,3       )
						    + self.x.view(A_    ,1    ,3       ) * fracs[:, :, :1]
						    + self.y.view(A_    ,1    ,3       ) * fracs[:, :, 1:]
						)
						UpperCAmelCase				   =  directions / directions.norm(dim=-1    ,keepdim=A_       )
						UpperCAmelCase				   =  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 	_UpperCAmelCase						(		self	: str    ,__SCREAMING_SNAKE_CASE	: str    ,__SCREAMING_SNAKE_CASE	: Union[str, Any]       ):
						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   __UpperCamelCase						(				_lowerCAmelCase ):
					"""simple docstring"""





					UpperCAmelCase				   =  []
					UpperCAmelCase				   =  []
					UpperCAmelCase				   =  []
					UpperCAmelCase				   =  []
					for theta in np.linspace(0	,						2 * np.pi	,						num=20 ):
										UpperCAmelCase				   =  np.array([np.sin(_lowerCAmelCase ), np.cos(_lowerCAmelCase ), -0.5] )
										z /= np.sqrt(np.sum(z**2 ) )
										UpperCAmelCase				   =  -z * 4
										UpperCAmelCase				   =  np.array([np.cos(_lowerCAmelCase ), -np.sin(_lowerCAmelCase ), 0.0] )
										UpperCAmelCase				   =  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 ))	,						)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase_												=	{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_												=	['OwlViTFeatureExtractor']
    UpperCamelCase_												=	['OwlViTImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_												=	[
        'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'OwlViTModel',
        'OwlViTPreTrainedModel',
        'OwlViTTextModel',
        'OwlViTVisionModel',
        'OwlViTForObjectDetection',
    ]

if TYPE_CHECKING:
    from .configuration_owlvit import (
        OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        OwlViTConfig,
        OwlViTOnnxConfig,
        OwlViTTextConfig,
        OwlViTVisionConfig,
    )
    from .processing_owlvit import OwlViTProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_owlvit import OwlViTFeatureExtractor
        from .image_processing_owlvit import OwlViTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_owlvit import (
            OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            OwlViTForObjectDetection,
            OwlViTModel,
            OwlViTPreTrainedModel,
            OwlViTTextModel,
            OwlViTVisionModel,
        )

else:
    import sys

    UpperCamelCase_												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



