



"""simple docstring"""

from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




@dataclass
class SCREAMING_SNAKE_CASE__					:
						_lowerCAmelCase							=					42  # [batch_size x 3]
						_lowerCAmelCase							=					42  # [batch_size x 3]
						_lowerCAmelCase							=					42  # [batch_size x 3]
						_lowerCAmelCase							=					42  # [batch_size x 3]
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					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'''

													__a		:		Union[str, Any]									=       torch.arange(self.height * self.width							)
													__a		:		Tuple									=       torch.stack(
													    [
													        pixel_indices % self.width,
													        torch.div(_lowercase						,     self.width						,     rounding_mode="""trunc"""							),
													    ]						,     axis=1						,     )
													return coords



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     *__a		:		Union[str, Any]									=       self.shape
													__a		:		int									=       int(np.prod(_lowercase							)							)

													__a		:		Union[str, Any]									=       self.get_image_coords()
													__a		:		List[Any]									=       torch.broadcast_to(coords.unsqueeze(0							)						,     [batch_size * inner_batch_size, *coords.shape]							)
													__a		:		List[str]									=       self.get_camera_rays(_lowercase							)

													__a		:		Union[str, Any]									=       rays.view(_lowercase						,     inner_batch_size * self.height * self.width						,     2						,     3							)

													return rays



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													__a							,     *__a							,     __a		:		str									=       coords.shape
													assert n_coords == 2
													assert batch_size == self.origin.shape[0]

													__a		:		List[str]									=       coords.view(_lowercase						,     -1						,     2							)

													__a		:		List[Any]									=       self.resolution()
													__a		:		List[Any]									=       self.fov()

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

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





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





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







def       __magic_name__    (   _lowerCamelCase				: int					):
							__a		:		int									=       []
							__a		:		str									=       []
							__a		:		Optional[int]									=       []
							__a		:		Optional[Any]									=       []
							for theta in np.linspace(0  ,       2 * np.pi  ,       num=2_0					):
														__a		:		str									=       np.array([np.sin(_lowerCamelCase					), np.cos(_lowerCamelCase					), -0.5]					)
														z /= np.sqrt(np.sum(z**2					)					)
														__a		:		Any									=       -z * 4
														__a		:		Union[str, Any]									=       np.array([np.cos(_lowerCamelCase					), -np.sin(_lowerCamelCase					), 0.0]					)
														__a		:		Dict									=       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					))  ,       )






"""simple docstring"""

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]=False					):
							__a		:		Dict									=       OmegaConf.load(_lowerCamelCase					)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase					)					)					)
							return config





def       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: Union[str, Any]=None  ,       _lowerCamelCase				: int=None					):
							if conf_path is None:
														__a		:		str									=       """./model_checkpoints/vqgan_only.yaml"""
							__a		:		List[Any]									=       load_config(_lowerCamelCase  ,       display=_lowerCamelCase					)
							__a		:		Dict									=       VQModel(**config.model.params					)
							if ckpt_path is None:
														__a		:		List[Any]									=       """./model_checkpoints/vqgan_only.pt"""
							__a		:		Tuple									=       torch.load(_lowerCamelCase  ,       map_location=_lowerCamelCase					)
							if ".ckpt" in ckpt_path:
														__a		:		List[str]									=       sd["""state_dict"""]
							model.load_state_dict(_lowerCamelCase  ,       strict=_lowerCamelCase					)
							model.to(_lowerCamelCase					)
							del sd
							return model





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: List[str]					):
							__a							,     __a							,     __a		:		Tuple									=       model.encode(_lowerCamelCase					)
							print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''					)
							__a		:		Union[str, Any]									=       model.decode(_lowerCamelCase					)
							return xrec





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Union[str, Any]=False					):
							__a							,     __a		:		Optional[Any]									=       string.rsplit("""."""  ,       1					)
							if reload:
														__a		:		Optional[Any]									=       importlib.import_module(_lowerCamelCase					)
														importlib.reload(_lowerCamelCase					)
							return getattr(importlib.import_module(_lowerCamelCase  ,       package=_lowerCamelCase					)  ,       cls					)





def       __magic_name__    (   _lowerCamelCase				: Any					):
							if "target" not in config:
														raise KeyError("""Expected key `target` to instantiate."""					)
							return get_obj_from_str(config["""target"""]					)(**config.get("""params"""  ,       {}					)					)





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: int=True  ,       _lowerCamelCase				: int=True					):
							__a		:		Union[str, Any]									=       instantiate_from_config(_lowerCamelCase					)
							if sd is not None:
														model.load_state_dict(_lowerCamelCase					)
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}







def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int					):
							# load the specified checkpoint
							if ckpt:
														__a		:		List[str]									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)
														__a		:		Any									=       pl_sd["""global_step"""]
														print(F'''loaded model from global step {global_step}.'''					)
							else:
														__a		:		List[Any]									=       {"""state_dict""": None}
														__a		:		Any									=       None
							__a		:		Union[str, Any]									=       load_model_from_config(config.model  ,       pl_sd["""state_dict"""]  ,       gpu=_lowerCamelCase  ,       eval_mode=_lowerCamelCase					)["""model"""]
							return model, global_step

