
'''simple docstring'''


from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

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

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

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

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[int]     =		torch.arange(self.height * self.width      )
								lowerCamelCase_					:      str     =		torch.stack(
								    [
								        pixel_indices % self.width,
								        torch.div(A			,   self.width			,   rounding_mode='''trunc'''      ),
								    ]			,   axis=1			,   )
								return coords

			@property
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_,      *lowerCamelCase_					:      List[str]     =		self.shape
								lowerCamelCase_					:      List[str]     =		int(np.prod(A      )      )

								lowerCamelCase_					:      int     =		self.get_image_coords()
								lowerCamelCase_					:      str     =		torch.broadcast_to(coords.unsqueeze(0      )			,   [batch_size * inner_batch_size, *coords.shape]      )
								lowerCamelCase_					:      Dict     =		self.get_camera_rays(A      )

								lowerCamelCase_					:      Any     =		rays.view(A			,   inner_batch_size * self.height * self.width			,   2			,   3      )

								return rays

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_,      *lowerCamelCase_,      lowerCamelCase_					:      str     =		coords.shape
								assert n_coords == 2
								assert batch_size == self.origin.shape[0]

								lowerCamelCase_					:      Dict     =		coords.view(A			,   -1			,   2      )

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

								lowerCamelCase_					:      Any     =		(flat.float() / (res - 1)) * 2 - 1
								lowerCamelCase_					:      Dict     =		fracs * torch.tan(fov / 2      )

								lowerCamelCase_					:      str     =		fracs.view(A			,   -1			,   2      )
								lowerCamelCase_					:      Any     =		(
								    self.z.view(A			,   1			,   3      )
								    + self.x.view(A			,   1			,   3      ) * fracs[:, :, :1]
								    + self.y.view(A			,   1			,   3      ) * fracs[:, :, 1:]
								)
								lowerCamelCase_					:      List[str]     =		directions / directions.norm(dim=-1			,   keepdim=A      )
								lowerCamelCase_					:      Tuple     =		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			,   A			,   A      ):
								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 lowercase_	(   _lowercase				)     ->							DifferentiableProjectiveCamera:
					'''simple docstring'''

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




'''simple docstring'''


import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__lowercase							:   Union[str, Any]					=     logging.get_logger(__name__)




def lowercase_	(   )     ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_					:      Optional[Any]     =		os.getenv('''SM_HP_MP_PARAMETERS'''			,       '''{}'''				)
					try:
										# Parse it and check the field "partitions" is included, it is required for model parallel.
										lowerCamelCase_					:      Optional[Any]     =		json.loads(_lowercase				)
										if "partitions" not in smp_options:
															return False
					except json.JSONDecodeError:
										return False

					# Get the sagemaker specific framework parameters from mpi_options variable.
					lowerCamelCase_					:      List[str]     =		os.getenv('''SM_FRAMEWORK_PARAMS'''			,       '''{}'''				)
					try:
										# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
										lowerCamelCase_					:      List[str]     =		json.loads(_lowercase				)
										if not mpi_options.get('''sagemaker_mpi_enabled'''			,       _lowercase				):
															return False
					except json.JSONDecodeError:
										return False
					# Lastly, check if the `smdistributed` module is present.
					return importlib.util.find_spec('''smdistributed'''				) is not None


if is_sagemaker_model_parallel_available():
					import smdistributed.modelparallel.torch as smp

					smp.init()






@dataclass
class 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		str	      =			field(
			    default=""      ,     metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}      ,     )

			def        UpperCAmelCase__ (self      ):
								super().__post_init__()
								warnings.warn(
								    '''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
								    '''`TrainingArguments` instead.'''			,   A			,   )

			@cached_property
			def        UpperCAmelCase__ (self      ):
								logger.info('''PyTorch: setting up devices'''      )
								if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
													logger.warning(
													    '''torch.distributed process group is initialized, but local_rank == -1. '''
													    '''In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'''      )
								if self.no_cuda:
													lowerCamelCase_					:      Optional[int]     =		torch.device('''cpu'''      )
													lowerCamelCase_					:      Dict     =		0
								elif is_sagemaker_model_parallel_available():
													lowerCamelCase_					:      int     =		smp.local_rank()
													lowerCamelCase_					:      str     =		torch.device('''cuda'''			,   A      )
													lowerCamelCase_					:      Tuple     =		1
								elif is_sagemaker_dp_enabled():
													import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

													torch.distributed.init_process_group(backend='''smddp'''			,   timeout=self.ddp_timeout_delta      )
													lowerCamelCase_					:      List[Any]     =		int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''      )      )
													lowerCamelCase_					:      Union[str, Any]     =		torch.device('''cuda'''			,   self.local_rank      )
													lowerCamelCase_					:      List[str]     =		1
								elif self.local_rank == -1:
													# if n_gpu is > 1 we'll use nn.DataParallel.
													# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
													# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
													# trigger an error that a device index is missing. Index 0 takes into account the
													# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
													# will use the first GPU in that env, i.e. GPU#1
													lowerCamelCase_					:      List[str]     =		torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''      )
													# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
													# the default value.
													lowerCamelCase_					:      Any     =		torch.cuda.device_count()
								else:
													# Here, we'll use torch.distributed.
													# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
													if not torch.distributed.is_initialized():
																		torch.distributed.init_process_group(backend='''nccl'''			,   timeout=self.ddp_timeout_delta      )
													lowerCamelCase_					:      Any     =		torch.device('''cuda'''			,   self.local_rank      )
													lowerCamelCase_					:      List[str]     =		1

								if device.type == "cuda":
													torch.cuda.set_device(A      )

								return device

			@property
			def        UpperCAmelCase__ (self      ):
								if is_sagemaker_model_parallel_available():
													return smp.dp_size()

								return super().world_size

			@property
			def        UpperCAmelCase__ (self      ):
								return not is_sagemaker_model_parallel_available()






			@property
			def        UpperCAmelCase__ (self      ):
								return False


