



'''simple docstring'''





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class     UpperCAmelCase_       :
    """simple docstring"""







    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 lowerCamelCase       (    self					:     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 lowerCamelCase       (    self					:     Union[str, Any]						):
          return torch.from_numpy(np.array([self.width, self.height] ,     dtype=np.floataa						)						)




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




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    str    						=      torch.arange(self.height * self.width						)
          snake_case__  :    Optional[Any]    						=      torch.stack(
              [
                  pixel_indices % self.width,
                  torch.div(snake_case_ ,     self.width ,     rounding_mode="""trunc"""						),
              ] ,     axis=1 ,     )
          return coords




    @property
    def lowerCamelCase       (    self					:     Tuple						):
          snake_case__						,				*snake_case__  :    List[Any]    						=      self.shape
          snake_case__  :    str    						=      int(np.prod(snake_case_						)						)

          snake_case__  :    List[Any]    						=      self.get_image_coords()
          snake_case__  :    Tuple    						=      torch.broadcast_to(coords.unsqueeze(0						) ,     [batch_size * inner_batch_size, *coords.shape]						)
          snake_case__  :    int    						=      self.get_camera_rays(snake_case_						)

          snake_case__  :    Tuple    						=      rays.view(snake_case_ ,     inner_batch_size * self.height * self.width ,     2 ,     3						)

          return rays




    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     torch.Tensor						):
          snake_case__						,				*snake_case__						,				snake_case__  :    Union[str, Any]    						=      coords.shape
          assert n_coords == 2
          assert batch_size == self.origin.shape[0]

          snake_case__  :    int    						=      coords.view(snake_case_ ,     -1 ,     2						)

          snake_case__  :    Dict    						=      self.resolution()
          snake_case__  :    List[str]    						=      self.fov()

          snake_case__  :    Union[str, Any]    						=      (flat.float() / (res - 1)) * 2 - 1
          snake_case__  :    str    						=      fracs * torch.tan(fov / 2						)

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




    def lowerCamelCase       (    self					:     Union[str, Any] ,     snake_case_					:     int ,     snake_case_					:     int						):
          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=snake_case_ ,     height=snake_case_ ,     x_fov=self.x_fov ,     y_fov=self.y_fov ,     )





def 						__snake_case(				_lowerCAmelCase		)		-> DifferentiableProjectiveCamera:
      snake_case__  :    Union[str, Any]    						=      []
      snake_case__  :    int    						=      []
      snake_case__  :    List[Any]    						=      []
      snake_case__  :    Tuple    						=      []
      for theta in np.linspace(0     ,   2 * np.pi     ,   num=20		):
            snake_case__  :    Any    						=      np.array([np.sin(_lowerCAmelCase		), np.cos(_lowerCAmelCase		), -0.5]		)
            z /= np.sqrt(np.sum(z**2		)		)
            snake_case__  :    Optional[int]    						=      -z * 4
            snake_case__  :    List[str]    						=      np.array([np.cos(_lowerCAmelCase		), -np.sin(_lowerCAmelCase		), 0.0]		)
            snake_case__  :    Optional[int]    						=      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'''

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser





def __snake_case					(      ):
 lowerCamelCase_      =  ArgumentParser("Accelerate CLI tool"					,				usage="accelerate <command> [<args>]"					,				allow_abbrev=UpperCAmelCase_		)
 lowerCamelCase_      =  parser.add_subparsers(help="accelerate command helpers"		)

 # Register commands
 get_config_parser(subparsers=UpperCAmelCase_		)
 env_command_parser(subparsers=UpperCAmelCase_		)
 launch_command_parser(subparsers=UpperCAmelCase_		)
 tpu_command_parser(subparsers=UpperCAmelCase_		)
 test_command_parser(subparsers=UpperCAmelCase_		)

 # Let's go
 lowerCamelCase_      =  parser.parse_args()

 if not hasattr(UpperCAmelCase_					,				"func"		):
  parser.print_help()
  exit(1		)

 # Run
 args.func(UpperCAmelCase_		)


if __name__ == "__main__":
     main()



