



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch
@dataclass
class    __UpperCamelCase			:

 """simple docstring"""



 _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 						_UpperCAmelCase       (	self      ) ->			int:
  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      ) ->			Optional[Any]:
  return torch.from_numpy(np.array([self.width, self.height]		,				dtype=np.floataa      )      )

 def 						_UpperCAmelCase       (	self      ) ->			str:
  return torch.from_numpy(np.array([self.x_fov, self.y_fov]		,				dtype=np.floataa      )      )

 def 						_UpperCAmelCase       (	self      ) ->			torch.Tensor:
  a__												= torch.arange(self.height * self.width      )
  a__												= torch.stack(
      [
          pixel_indices % self.width,
          torch.div(SCREAMING_SNAKE_CASE		,				self.width		,				rounding_mode='''trunc'''      ),
      ]		,				axis=1		,				)
  return coords

 @property
 def 						_UpperCAmelCase       (	self      ) ->			Dict:
  a__							,			*a__												= self.shape
  a__												= int(np.prod(SCREAMING_SNAKE_CASE      )      )

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

  a__												= rays.view(SCREAMING_SNAKE_CASE		,				inner_batch_size * self.height * self.width		,				2		,				3      )

  return rays

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			torch.Tensor:
  a__							,			*a__							,			a__												= coords.shape
  assert n_coords == 2
  assert batch_size == self.origin.shape[0]

  a__												= coords.view(SCREAMING_SNAKE_CASE		,				-1		,				2      )

  a__												= self.resolution()
  a__												= self.fov()

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

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



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			"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=SCREAMING_SNAKE_CASE		,				height=SCREAMING_SNAKE_CASE		,				x_fov=self.x_fov		,				y_fov=self.y_fov		,				)






def     __a					(		__UpperCAmelCase       ):
 a__												= []
 a__												= []
 a__												= []
 a__												= []
 for theta in np.linspace(0							,  2 * np.pi							,  num=20       ):
  a__												= np.array([np.sin(__UpperCAmelCase       ), np.cos(__UpperCAmelCase       ), -0.5]       )
  z /= np.sqrt(np.sum(z**2       )       )
  a__												= -z * 4
  a__												= np.array([np.cos(__UpperCAmelCase       ), -np.sin(__UpperCAmelCase       ), 0.0]       )
  a__												= 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 warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    Optional[Any]      						=					['''image_processor''', '''tokenizer''']
 _lowercase   :    Optional[int]      						=					'''ChineseCLIPImageProcessor'''
 _lowercase   :    List[Any]      						=					('''BertTokenizer''', '''BertTokenizerFast''')

 def __init__(	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__												= None
  if "feature_extractor" in kwargs:
   warnings.warn(
       '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
       ''' instead.'''		,				SCREAMING_SNAKE_CASE		,				)
   a__												= kwargs.pop('''feature_extractor'''      )

  a__												= image_processor if image_processor is not None else feature_extractor
  if image_processor is None:
   raise ValueError('''You need to specify an `image_processor`.'''      )
  if tokenizer is None:
   raise ValueError('''You need to specify a `tokenizer`.'''      )

  super().__init__(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  a__												= self.image_processor

 def __call__(	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			List[str]:

  if text is None and images is None:
   raise ValueError('''You have to specify either text or images. Both cannot be none.'''      )

  if text is not None:
   a__												= self.tokenizer(SCREAMING_SNAKE_CASE		,				return_tensors=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  if images is not None:
   a__												= self.image_processor(SCREAMING_SNAKE_CASE		,				return_tensors=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  if text is not None and images is not None:
   a__												= image_features.pixel_values
   return encoding
  elif text is not None:
   return encoding
  else:
   return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE      )		,				tensor_type=SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			List[str]:
  return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			int:
  return self.tokenizer.decode(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

 @property
 def 						_UpperCAmelCase       (	self      ) ->			List[str]:
  a__												= self.tokenizer.model_input_names
  a__												= self.image_processor.model_input_names
  return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )



 @property
 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  warnings.warn(
      '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,				SCREAMING_SNAKE_CASE		,				)
  return self.image_processor_class





