





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

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


      def        a			(self	:				List[str]				):





         """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        a			(self	:				Optional[int]				):





         """simple docstring"""



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


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



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


      def        a			(self	:				List[Any]				):





         """simple docstring"""



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


      @property
      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         __snake_case	,     *__snake_case								=       self.shape
         __snake_case								=       int(np.prod(a__				)				)

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

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

         return rays


      def        a			(self	:				Optional[int]    ,      a__	:				torch.Tensor				):





         """simple docstring"""



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

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

         __snake_case								=       self.resolution()
         __snake_case								=       self.fov()

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

         __snake_case								=       fracs.view(a__    ,      -1    ,      2				)
         __snake_case								=       (
             self.z.view(a__    ,      1    ,      3				)
             + self.x.view(a__    ,      1    ,      3				) * fracs[:, :, :1]
             + self.y.view(a__    ,      1    ,      3				) * fracs[:, :, 1:]
         )
         __snake_case								=       directions / directions.norm(dim=-1    ,      keepdim=a__				)
         __snake_case								=       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        a			(self	:				Any    ,      a__	:				int    ,      a__	:				int				):





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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_           =      {
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =      [
        'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'GitForCausalLM',
        'GitModel',
        'GitPreTrainedModel',
        'GitVisionModel',
    ]

if TYPE_CHECKING:
    from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
    from .processing_git import GitProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_git import (
            GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GitForCausalLM,
            GitModel,
            GitPreTrainedModel,
            GitVisionModel,
        )

else:
    import sys

    snake_case_           =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

