


from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch


@dataclass
class 	_a							:
							_lowercase     :      torch.Tensor  # [batch_size x 3]
							_lowercase     :      torch.Tensor  # [batch_size x 3]
							_lowercase     :      torch.Tensor  # [batch_size x 3]
							_lowercase     :      torch.Tensor  # [batch_size x 3]
							_lowercase     :      int
							_lowercase     :      int
							_lowercase     :      float
							_lowercase     :      float
							_lowercase     :      Tuple[int]

							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       List[Any]:





									"""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:     Union[str, Any]							)      ->       Union[str, Any]:





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       int:





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       torch.Tensor:





									"""simple docstring"""







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

							@property
							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       Optional[int]:





									"""simple docstring"""







									lowercase__					,		*lowercase__						=				self.shape
									lowercase__						=				int(np.prod(UpperCamelCase_							)							)

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

									lowercase__						=				rays.view(UpperCamelCase_   ,						inner_batch_size * self.height * self.width   ,						2   ,						3							)

									return rays

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     torch.Tensor							)      ->       torch.Tensor:





									"""simple docstring"""







									lowercase__					,		*lowercase__					,		lowercase__						=				coords.shape
									assert n_coords == 2
									assert batch_size == self.origin.shape[0]

									lowercase__						=				coords.view(UpperCamelCase_   ,						-1   ,						2							)

									lowercase__						=				self.resolution()
									lowercase__						=				self.fov()

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

									lowercase__						=				fracs.view(UpperCamelCase_   ,						-1   ,						2							)
									lowercase__						=				(
									    self.z.view(UpperCamelCase_   ,						1   ,						3							)
									    + self.x.view(UpperCamelCase_   ,						1   ,						3							) * fracs[:, :, :1]
									    + self.y.view(UpperCamelCase_   ,						1   ,						3							) * fracs[:, :, 1:]
									)
									lowercase__						=				directions / directions.norm(dim=-1   ,						keepdim=UpperCamelCase_							)
									lowercase__						=				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:     Any   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     int							)      ->       "DifferentiableProjectiveCamera":





									"""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			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




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






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
			import torch

			from transformers.activations import gelu_new, gelu_python, get_activation


@require_torch
class 	_a							(      unittest.TestCase      ):

							def 				lowerCamelCase_			(							self:     Dict							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]							)
									lowercase__						=				get_activation('''gelu'''							)
									self.assertTrue(torch.allclose(gelu_python(UpperCamelCase_							)   ,						torch_builtin(UpperCamelCase_							)							)							)
									self.assertFalse(torch.allclose(gelu_python(UpperCamelCase_							)   ,						gelu_new(UpperCamelCase_							)							)							)

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]							)
									lowercase__						=				get_activation('''gelu'''							)
									lowercase__						=				get_activation('''gelu_10'''							)

									lowercase__						=				torch_builtin(UpperCamelCase_							)
									lowercase__						=				geluaa(UpperCamelCase_							)

									lowercase__						=				torch.where(y_gelu_aa < 10.0   ,						1   ,						0							)

									self.assertTrue(torch.max(UpperCamelCase_							).item() == 10.0							)
									self.assertTrue(torch.allclose(y_gelu * clipped_mask   ,						y_gelu_aa * clipped_mask							)							)

							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       Tuple:





									"""simple docstring"""







									get_activation('''gelu'''							)
									get_activation('''gelu_10'''							)
									get_activation('''gelu_fast'''							)
									get_activation('''gelu_new'''							)
									get_activation('''gelu_python'''							)
									get_activation('''gelu_pytorch_tanh'''							)
									get_activation('''linear'''							)
									get_activation('''mish'''							)
									get_activation('''quick_gelu'''							)
									get_activation('''relu'''							)
									get_activation('''sigmoid'''							)
									get_activation('''silu'''							)
									get_activation('''swish'''							)
									get_activation('''tanh'''							)
									with self.assertRaises(UpperCamelCase_							):
											get_activation('''bogus'''							)
									with self.assertRaises(UpperCamelCase_							):
											get_activation(UpperCamelCase_							)


							def 				lowerCamelCase_			(							self:     Tuple							)      ->       int:





									"""simple docstring"""







									lowercase__						=				get_activation('''gelu'''							)
									lowercase__						=				1
									lowercase__						=				get_activation('''gelu'''							)
									self.assertEqual(acta.a   ,						1							)
									with self.assertRaises(UpperCamelCase_							):
											lowercase__						=				acta.a


