


from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class        a	:




							"""simple docstring"""


							__UpperCAmelCase					:							torch.Tensor  # [batch_size x 3]
							__UpperCAmelCase					:							torch.Tensor  # [batch_size x 3]
							__UpperCAmelCase					:							torch.Tensor  # [batch_size x 3]
							__UpperCAmelCase					:							torch.Tensor  # [batch_size x 3]
							__UpperCAmelCase					:							int
							__UpperCAmelCase					:							int
							__UpperCAmelCase					:							float
							__UpperCAmelCase					:							float
							__UpperCAmelCase					:							Tuple[int]

							def 					__snake_case      (							self :						str       )			->					List[str]:
														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 					__snake_case      (							self :						List[str]       )			->					Union[str, Any]:
														return torch.from_numpy(np.array([self.width, self.height]					,			dtype=np.floataa       )       )

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

							def 					__snake_case      (							self :						Union[str, Any]       )			->					torch.Tensor:
														__snake_case	:    Dict											=     torch.arange(self.height * self.width       )
														__snake_case	:    Union[str, Any]											=     torch.stack(
														    [
														        pixel_indices % self.width,
														        torch.div(lowerCamelCase					,			self.width					,			rounding_mode="trunc"       ),
														    ]					,			axis=1					,			)
														return coords

							@property
							def 					__snake_case      (							self :						int       )			->					Optional[Any]:
														__snake_case				,    *__snake_case	:    str											=     self.shape
														__snake_case	:    Tuple											=     int(np.prod(lowerCamelCase       )       )

														__snake_case	:    Optional[Any]											=     self.get_image_coords()
														__snake_case	:    Any											=     torch.broadcast_to(coords.unsqueeze(0       )					,			[batch_size * inner_batch_size, *coords.shape]       )
														__snake_case	:    Tuple											=     self.get_camera_rays(lowerCamelCase       )

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

														return rays

							def 					__snake_case      (							self :						Tuple					,			lowerCamelCase :						torch.Tensor       )			->					torch.Tensor:
														__snake_case				,    *__snake_case				,    __snake_case	:    List[Any]											=     coords.shape
														assert n_coords == 2
														assert batch_size == self.origin.shape[0]

														__snake_case	:    Optional[Any]											=     coords.view(lowerCamelCase					,			-1					,			2       )

														__snake_case	:    List[Any]											=     self.resolution()
														__snake_case	:    Optional[int]											=     self.fov()

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

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






							def 					__snake_case      (							self :						Dict					,			lowerCamelCase :						int					,			lowerCamelCase :						int       )			->					"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=lowerCamelCase					,			height=lowerCamelCase					,			x_fov=self.x_fov					,			y_fov=self.y_fov					,			)







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    Optional[int]											=     []
							__snake_case	:    str											=     []
							__snake_case	:    Optional[int]											=     []
							__snake_case	:    Tuple											=     []
							for theta in np.linspace(0							,			2 * np.pi							,			num=2_0			):
														__snake_case	:    int											=     np.array([np.sin(__lowerCamelCase			), np.cos(__lowerCamelCase			), -0.5]			)
														z /= np.sqrt(np.sum(z**2			)			)
														__snake_case	:    Union[str, Any]											=     -z * 4
														__snake_case	:    Any											=     np.array([np.cos(__lowerCamelCase			), -np.sin(__lowerCamelCase			), 0.0]			)
														__snake_case	:    Optional[Any]											=     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			))							,			)





import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
			from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm







def        lowerCAmelCase_  (      __lowerCamelCase			):
							if is_torch_version("<"							,			"2.0.0"			) or not hasattr(__lowerCamelCase							,			"_dynamo"			):
														return False
							return isinstance(__lowerCamelCase							,			torch._dynamo.eval_frame.OptimizedModule			)







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase = True			):
							__snake_case	:    Optional[Any]											=     (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

							__snake_case	:    Tuple											=     is_compiled_module(__lowerCamelCase			)
							if is_compiled:
														__snake_case	:    Tuple											=     model
														__snake_case	:    List[str]											=     model._orig_mod

							if is_deepspeed_available():
														options += (DeepSpeedEngine,)

							while isinstance(__lowerCamelCase							,			__lowerCamelCase			):
														__snake_case	:    List[Any]											=     model.module

							if not keep_fpaa_wrapper:
														__snake_case	:    Tuple											=     getattr(__lowerCamelCase							,			"forward"			)
														__snake_case	:    int											=     model.__dict__.pop("_original_forward"							,			__lowerCamelCase			)
														if original_forward is not None:
																					while hasattr(__lowerCamelCase							,			"__wrapped__"			):
																												__snake_case	:    str											=     forward.__wrapped__
																												if forward == original_forward:
																																			break
																					__snake_case	:    Dict											=     forward
														if getattr(__lowerCamelCase							,			"_converted_to_transformer_engine"							,			__lowerCamelCase			):
																					convert_model(__lowerCamelCase							,			to_transformer_engine=__lowerCamelCase			)

							if is_compiled:
														__snake_case	:    Tuple											=     model
														__snake_case	:    str											=     compiled_model

							return model







def        lowerCAmelCase_  (      ):
							PartialState().wait_for_everyone()







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							if PartialState().distributed_type == DistributedType.TPU:
														xm.save(__lowerCamelCase							,			__lowerCamelCase			)
							elif PartialState().local_process_index == 0:
														torch.save(__lowerCamelCase							,			__lowerCamelCase			)







@contextmanager
def        lowerCAmelCase_  (      **__lowerCamelCase			):
							for key, value in kwargs.items():
														__snake_case	:    Union[str, Any]											=     str(__lowerCamelCase			)

							yield

							for key in kwargs:
														if key.upper() in os.environ:
																					del os.environ[key.upper()]







def        lowerCAmelCase_  (      __lowerCamelCase			):
							if not hasattr(__lowerCamelCase							,			"__qualname__"			) and not hasattr(__lowerCamelCase							,			"__name__"			):
														__snake_case	:    Dict											=     getattr(__lowerCamelCase							,			"__class__"							,			__lowerCamelCase			)
							if hasattr(__lowerCamelCase							,			"__qualname__"			):
														return obj.__qualname__
							if hasattr(__lowerCamelCase							,			"__name__"			):
														return obj.__name__
							return str(__lowerCamelCase			)







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							for key, value in source.items():
														if isinstance(__lowerCamelCase							,			__lowerCamelCase			):
																					__snake_case	:    Tuple											=     destination.setdefault(__lowerCamelCase							,			{}			)
																					merge_dicts(__lowerCamelCase							,			__lowerCamelCase			)
														else:
																					__snake_case	:    Union[str, Any]											=     value

							return destination







def        lowerCAmelCase_  (      __lowerCamelCase = None			):
							if port is None:
														__snake_case	:    List[str]											=     2_9_5_0_0
							with socket.socket(socket.AF_INET							,			socket.SOCK_STREAM			) as s:
														return s.connect_ex(("localhost", port)			) == 0

