
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




@dataclass
class  lowerCamelCase__							:




	'''simple docstring'''




	_A								=     42  # [batch_size x 3]
	_A								=     42  # [batch_size x 3]
	_A								=     42  # [batch_size x 3]
	_A								=     42  # [batch_size x 3]
	_A								=     42
	_A								=     42
	_A								=     42
	_A								=     42
	_A								=     42







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Optional[int]:
								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		:str						)		-> Any:
								return torch.from_numpy(np.array([self.width, self.height]     ,   dtype=np.floataa						)						)







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







	def    _lowerCamelCase				(					self		:str						)		-> torch.Tensor:
								__UpperCamelCase	:   Any			   =					torch.arange(self.height * self.width						)
								__UpperCamelCase	:   Optional[int]			   =					torch.stack(
								    [
								        pixel_indices % self.width,
								        torch.div(__snake_case     ,   self.width     ,   rounding_mode="trunc"						),
								    ]     ,   axis=1     ,   )
								return coords







	@property
	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Any:
								__UpperCamelCase   ,						*__UpperCamelCase	:   Optional[int]			   =					self.shape
								__UpperCamelCase	:   List[str]			   =					int(np.prod(__snake_case						)						)

								__UpperCamelCase	:   int			   =					self.get_image_coords()
								__UpperCamelCase	:   Optional[int]			   =					torch.broadcast_to(coords.unsqueeze(0						)     ,   [batch_size * inner_batch_size, *coords.shape]						)
								__UpperCamelCase	:   Optional[Any]			   =					self.get_camera_rays(__snake_case						)

								__UpperCamelCase	:   List[str]			   =					rays.view(__snake_case     ,   inner_batch_size * self.height * self.width     ,   2     ,   3						)

								return rays







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:torch.Tensor						)		-> torch.Tensor:
								__UpperCamelCase   ,						*__UpperCamelCase   ,						__UpperCamelCase	:   Any			   =					coords.shape
								assert n_coords == 2
								assert batch_size == self.origin.shape[0]

								__UpperCamelCase	:   Dict			   =					coords.view(__snake_case     ,   -1     ,   2						)

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

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

								__UpperCamelCase	:   Optional[int]			   =					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	:   Optional[Any]			   =					directions / directions.norm(dim=-1     ,   keepdim=__snake_case						)
								__UpperCamelCase	:   Dict			   =					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    _lowerCamelCase				(					self		:Dict     ,   a		:int     ,   a		:int						)		-> "DifferentiableProjectiveCamera":
								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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int)					->    Dict:






							'''simple docstring'''





							__UpperCamelCase	:   List[str]			   =					[]
							__UpperCamelCase	:   Dict			   =					[]
							__UpperCamelCase	:   int			   =					[]
							__UpperCamelCase	:   Union[str, Any]			   =					[]
							for theta in np.linspace(0						,  2 * np.pi						,  num=20):
														__UpperCamelCase	:   List[Any]			   =					np.array([np.sin(__lowerCAmelCase), np.cos(__lowerCAmelCase), -0.5])
														z /= np.sqrt(np.sum(z**2))
														__UpperCamelCase	:   Tuple			   =					-z * 4
														__UpperCamelCase	:   Tuple			   =					np.array([np.cos(__lowerCAmelCase), -np.sin(__lowerCAmelCase), 0.0])
														__UpperCamelCase	:   Any			   =					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 argparse import ArgumentParser

from .env import EnvironmentCommand






def 							__lowercase   ( ):
							a__             =     ArgumentParser('Diffusers CLI tool'					,    usage='diffusers-cli <command> [<args>]'   )
							a__             =     parser.add_subparsers(help='diffusers-cli command helpers'   )

							# Register commands
							EnvironmentCommand.register_subcommand(__lowerCAmelCase   )

							# Let's go
							a__             =     parser.parse_args()

							if not hasattr(__lowerCAmelCase					,    'func'   ):
														parser.print_help()
														exit(1   )

							# Run
							a__             =     args.func(__lowerCAmelCase   )
							service.run()


if __name__ == "__main__":
						main()




