


"""simple docstring"""







from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch


@dataclass
class  SCREAMING_SNAKE_CASE__							:
       """simple docstring"""


       a    :							Tuple  =42  # [batch_size x 3]
       a    :							Optional[Any]  =42  # [batch_size x 3]
       a    :							Dict  =42  # [batch_size x 3]
       a    :							int  =42  # [batch_size x 3]
       a    :							Dict  =42
       a    :							Optional[Any]  =42
       a    :							Optional[int]  =42
       a    :							List[Any]  =42
       a    :							Tuple  =42

       def 			lowercase__				(		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 			lowercase__				(		self		):






         """simple docstring"""





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

       def 			lowercase__				(		self		):






         """simple docstring"""





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

       def 			lowercase__				(		self		):






         """simple docstring"""





         lowerCAmelCase	:	List[str]								=				torch.arange(self.height * self.width		)
         lowerCAmelCase	:	Optional[int]								=				torch.stack(
             [
                 pixel_indices % self.width,
                 torch.div(a__						,	self.width						,	rounding_mode="trunc"		),
             ]						,	axis=1						,	)
         return coords

       @property
       def 			lowercase__				(		self		):






         """simple docstring"""





         lowerCAmelCase		,    *lowerCAmelCase	:	int								=				self.shape
         lowerCAmelCase	:	Dict								=				int(np.prod(a__		)		)

         lowerCAmelCase	:	str								=				self.get_image_coords()
         lowerCAmelCase	:	int								=				torch.broadcast_to(coords.unsqueeze(0		)						,	[batch_size * inner_batch_size, *coords.shape]		)
         lowerCAmelCase	:	Dict								=				self.get_camera_rays(a__		)

         lowerCAmelCase	:	Optional[Any]								=				rays.view(a__						,	inner_batch_size * self.height * self.width						,	2						,	3		)

         return rays

       def 			lowercase__				(		self						,	snake_case__		):






         """simple docstring"""





         lowerCAmelCase		,    *lowerCAmelCase		,    lowerCAmelCase	:	Optional[Any]								=				coords.shape
         assert n_coords == 2
         assert batch_size == self.origin.shape[0]

         lowerCAmelCase	:	str								=				coords.view(a__						,	-1						,	2		)

         lowerCAmelCase	:	Dict								=				self.resolution()
         lowerCAmelCase	:	Union[str, Any]								=				self.fov()

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

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







       def 			lowercase__				(		self						,	snake_case__						,	snake_case__		):






         """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=a__						,	height=a__						,	x_fov=self.x_fov						,	y_fov=self.y_fov						,	)

def    a__     (							SCREAMING_SNAKE_CASE       :       int							):


  '''simple docstring'''







  lowerCAmelCase	:	Any								=				[]
  lowerCAmelCase	:	int								=				[]
  lowerCAmelCase	:	Optional[int]								=				[]
  lowerCAmelCase	:	str								=				[]
  for theta in np.linspace(0							, 2 * np.pi							, num=2_0							):
    lowerCAmelCase	:	Optional[Any]								=				np.array([np.sin(UpperCamelCase__							), np.cos(UpperCamelCase__							), -0.5]							)
    z /= np.sqrt(np.sum(z**2							)							)
    lowerCAmelCase	:	List[Any]								=				-z * 4
    lowerCAmelCase	:	Dict								=				np.array([np.cos(UpperCamelCase__							), -np.sin(UpperCamelCase__							), 0.0]							)
    lowerCAmelCase	:	List[Any]								=				np.cross(UpperCamelCase__							, UpperCamelCase__							)
    origins.append(UpperCamelCase__							)
    xs.append(UpperCamelCase__							)
    ys.append(UpperCamelCase__							)
    zs.append(UpperCamelCase__							)
  return DifferentiableProjectiveCamera(
      origin=torch.from_numpy(np.stack(UpperCamelCase__							, axis=0							)							).float()							, x=torch.from_numpy(np.stack(UpperCamelCase__							, axis=0							)							).float()							, y=torch.from_numpy(np.stack(UpperCamelCase__							, axis=0							)							).float()							, z=torch.from_numpy(np.stack(UpperCamelCase__							, axis=0							)							).float()							, width=UpperCamelCase__							, height=UpperCamelCase__							, x_fov=0.7							, y_fov=0.7							, shape=(1, len(UpperCamelCase__							))							, )









import numpy as np

from transformers import Pipeline





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]    )			-> Optional[int]:





	"""simple docstring"""







	A             =					np.max(UpperCamelCase__				,       axis=-1				,       keepdims=UpperCamelCase__    )
	A             =					np.exp(outputs - maxes    )
	return shifted_exp / shifted_exp.sum(axis=-1				,       keepdims=UpperCamelCase__    )






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def   _UpperCAmelCase       (    self					, **a__ )						->					Union[str, Any]:
					A             =					{}
					if "second_text" in kwargs:
						A             =					kwargs["""second_text"""]
					return preprocess_kwargs, {}, {}

				def   _UpperCAmelCase       (    self					, a__					, a__=None )						->					str:
					return self.tokenizer(a__					, text_pair=a__					, return_tensors=self.framework )

				def   _UpperCAmelCase       (    self					, a__ )						->					Tuple:
					return self.model(**a__ )

				def   _UpperCAmelCase       (    self					, a__ )						->					Optional[int]:
					A             =					model_outputs.logits[0].numpy()
					A             =					softmax(a__ )

					A             =					np.argmax(a__ )
					A             =					self.model.config.idalabel[best_class]
					A             =					probabilities[best_class].item()
					A             =					logits.tolist()
					return {"label": label, "score": score, "logits": logits}



