





'''simple docstring'''

from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch






@dataclass
class        UpperCAmelCase     :
 __lowercase       		=    42  # [batch_size x 3]
 __lowercase       		=    42  # [batch_size x 3]
 __lowercase       		=    42  # [batch_size x 3]
 __lowercase       		=    42  # [batch_size x 3]
 __lowercase       		=    42
 __lowercase       		=    42
 __lowercase       		=    42
 __lowercase       		=    42
 __lowercase       		=    42
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Optional[Any]:
     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				:Any							)->							Optional[Any]:
     return torch.from_numpy(np.array([self.width, self.height]						,	dtype=np.floataa							)							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Union[str, Any]:
     return torch.from_numpy(np.array([self.x_fov, self.y_fov]						,	dtype=np.floataa							)							)
 def 					UpperCAmelCase_							(    self				:int							)->							torch.Tensor:
     A__    =      torch.arange(self.height * self.width							)
     A__    =      torch.stack(
         [
             pixel_indices % self.width,
             torch.div(lowercase_						,	self.width						,	rounding_mode="trunc"							),
         ]						,	axis=1						,	)
     return coords
 @property
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Dict:
     A__,     *A__    =      self.shape
     A__    =      int(np.prod(lowercase_							)							)

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

     A__    =      rays.view(lowercase_						,	inner_batch_size * self.height * self.width						,	2						,	3							)

     return rays
 def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:torch.Tensor							)->							torch.Tensor:
     A__,     *A__,     A__    =      coords.shape
     assert n_coords == 2
     assert batch_size == self.origin.shape[0]

     A__    =      coords.view(lowercase_						,	-1						,	2							)

     A__    =      self.resolution()
     A__    =      self.fov()

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

     A__    =      fracs.view(lowercase_						,	-1						,	2							)
     A__    =      (
         self.z.view(lowercase_						,	1						,	3							)
         + self.x.view(lowercase_						,	1						,	3							) * fracs[:, :, :1]
         + self.y.view(lowercase_						,	1						,	3							) * fracs[:, :, 1:]
     )
     A__    =      directions / directions.norm(dim=-1						,	keepdim=lowercase_							)
     A__    =      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 					UpperCAmelCase_							(    self				:int						,	lowercase_				:int						,	lowercase_				: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=lowercase_						,	height=lowercase_						,	x_fov=self.x_fov						,	y_fov=self.y_fov						,	)





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    A__    =      []
    A__    =      []
    A__    =      []
    A__    =      []
    for theta in np.linspace(0						,	2 * np.pi						,	num=20	):
        A__    =      np.array([np.sin(_lowerCamelCase	), np.cos(_lowerCamelCase	), -0.5]	)
        z /= np.sqrt(np.sum(z**2	)	)
        A__    =      -z * 4
        A__    =      np.array([np.cos(_lowerCamelCase	), -np.sin(_lowerCamelCase	), 0.0]	)
        A__    =      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'''

from .imports import is_tqdm_available


if is_tqdm_available():
  from tqdm.auto import tqdm as _tqdm

from ..state import PartialState





def 						UpperCamelCase    (							_lowerCamelCase : bool = True						,	*_lowerCamelCase : Optional[int]						,	**_lowerCamelCase : Optional[Any]	):
    if not is_tqdm_available():
        raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."	)
    A__    =      False
    if main_process_only:
        A__    =      PartialState().local_process_index == 0
    return _tqdm(*_lowerCamelCase						,	**_lowerCamelCase						,	disable=_lowerCamelCase	)



