


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

import numpy as np
import torch
@dataclass
class        lowercase     :

		"""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							):

							'''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    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[int]							 =			self.shape
							UpperCamelCase__       :int							 =			int(np.prod(UpperCamelCase_							)							)

							UpperCamelCase__       :Dict							 =			self.get_image_coords()
							UpperCamelCase__       :Tuple							 =			torch.broadcast_to(coords.unsqueeze(0							)   ,       [batch_size * inner_batch_size, *coords.shape]							)
							UpperCamelCase__       :List[Any]							 =			self.get_camera_rays(UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			rays.view(UpperCamelCase_   ,       inner_batch_size * self.height * self.width   ,       2   ,       3							)

							return rays




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			coords.shape
							assert n_coords == 2
							assert batch_size == self.origin.shape[0]

							UpperCamelCase__       :str							 =			coords.view(UpperCamelCase_   ,       -1   ,       2							)

							UpperCamelCase__       :List[str]							 =			self.resolution()
							UpperCamelCase__       :List[str]							 =			self.fov()

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

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




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''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=UpperCamelCase_   ,       height=UpperCamelCase_   ,       x_fov=self.x_fov   ,       y_fov=self.y_fov   ,       )



def       a				(      __a       ):



					'''simple docstring'''






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



'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser


def       a				(      __a=None       )  ->     List[str]:



					'''simple docstring'''






					UpperCamelCase__       :Optional[Any]							 =			argparse.ArgumentParser(add_help=__a			, allow_abbrev=__a       )
					# The main config parser
					UpperCamelCase__       :str							 =			config_command_parser(__a       )
					# The subparser to add commands to
					UpperCamelCase__       :Union[str, Any]							 =			config_parser.add_subparsers(title='''subcommands'''			, dest='''subcommand'''       )

					# Then add other parsers with the parent parser
					default_command_parser(__a			, parents=[parent_parser]       )
					update_command_parser(__a			, parents=[parent_parser]       )

					return config_parser


def       a				(      )  ->     Any:



					'''simple docstring'''






					UpperCamelCase__       :int							 =			get_config_parser()
					UpperCamelCase__       :List[Any]							 =			config_parser.parse_args()

					if not hasattr(__a			, '''func'''       ):
										config_parser.print_help()
										exit(1       )

					# Run
					args.func(__a       )


if __name__ == "__main__":
		main()