
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class 				UpperCAmelCase :




	'''simple docstring'''





	snake_case_          =	42  # [batch_size x 3]
	snake_case_          =	42  # [batch_size x 3]
	snake_case_          =	42  # [batch_size x 3]
	snake_case_          =	42  # [batch_size x 3]
	snake_case_          =	42
	snake_case_          =	42
	snake_case_          =	42
	snake_case_          =	42
	snake_case_          =	42






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






	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					return torch.from_numpy(np.array([self.x_fov, self.y_fov]				,dtype=np.floataa					)					)






	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					torch.arange(self.height * self.width					)
					__A       =					torch.stack(
					    [
					        pixel_indices % self.width,
					        torch.div(A				,self.width				,rounding_mode="trunc"					),
					    ]				,axis=1				,)
					return coords






	@property
	def 							UpperCamelCase_    (			self   :					Dict					):
					__A     ,				*__A       =					self.shape
					__A       =					int(np.prod(A					)					)

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

					__A       =					rays.view(A				,inner_batch_size * self.height * self.width				,2				,3					)

					return rays






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

					__A       =					coords.view(A				,-1				,2					)

					__A       =					self.resolution()
					__A       =					self.fov()

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

					__A       =					fracs.view(A				,-1				,2					)
					__A       =					(
					    self.z.view(A				,1				,3					)
					    + self.x.view(A				,1				,3					) * fracs[:, :, :1]
					    + self.y.view(A				,1				,3					) * fracs[:, :, 1:]
					)
					__A       =					directions / directions.norm(dim=-1				,keepdim=A					)
					__A       =					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 							UpperCamelCase_    (			self   :					Any				,A   :					int				,A   :					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=A				,height=A				,x_fov=self.x_fov				,y_fov=self.y_fov				,)





def 		UpperCAmelCase     (      a_		)						->      Union[str, Any]:







				"""simple docstring"""



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





# 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.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def 	_lowerCAmelCase (    A__=None						):
      if subparsers is not None:
            lowercase__  =	subparsers.add_parser('test'						)
      else:
            lowercase__  =	argparse.ArgumentParser('Accelerate test command'						)

      parser.add_argument(
          '--config_file'    ,	default=A__    ,	help=(
              'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
              'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
              'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
              'with \'huggingface\'.'
          )    ,	)

      if subparsers is not None:
            parser.set_defaults(func=A__						)
      return parser

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	os.path.sep.join(__file__.split(os.path.sep						)[:-2] + ['test_utils', 'scripts', 'test_script.py']						)

      if args.config_file is None:
            lowercase__  =	script_name
      else:
            lowercase__  =	F'''--config_file={args.config_file} {script_name}'''

      lowercase__  =	['accelerate-launch'] + test_args.split()
      lowercase__  =	execute_subprocess_async(A__    ,	env=os.environ.copy()						)
      if result.returncode == 0:
            print('Test is a success! You are ready for your distributed training!'						)

def 	_lowerCAmelCase (    ):
      lowercase__  =	test_command_parser()
      lowercase__  =	parser.parse_args()
      test_command(A__						)


if __name__ == "__main__":
       main()
