






'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_A	:			Dict         =logging.get_logger(__name__)

_A	:			Any         ={
    '''microsoft/table-transformer-detection''': (
        '''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
    ),
}

class _lowercase	(				_lowercase		):
							a				      =       """table-transformer"""
							a				      =       ["""past_key_values"""]
							a				      =       {
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """encoder_attention_heads""",
							}




							def __init__(							self:				Dict			,							UpperCamelCase__:				Any=True			,							UpperCamelCase__:				str=None			,							UpperCamelCase__:				List[Any]=3			,							UpperCamelCase__:				Any=100			,							UpperCamelCase__:				str=6			,							UpperCamelCase__:				Tuple=2_048			,							UpperCamelCase__:				Any=8			,							UpperCamelCase__:				List[str]=6			,							UpperCamelCase__:				Union[str, Any]=2_048			,							UpperCamelCase__:				Dict=8			,							UpperCamelCase__:				List[Any]=0.0			,							UpperCamelCase__:				Optional[int]=0.0			,							UpperCamelCase__:				Dict=True			,							UpperCamelCase__:				List[str]="relu"			,							UpperCamelCase__:				Optional[Any]=256			,							UpperCamelCase__:				Dict=0.1			,							UpperCamelCase__:				Optional[Any]=0.0			,							UpperCamelCase__:				str=0.0			,							UpperCamelCase__:				List[str]=0.02			,							UpperCamelCase__:				Union[str, Any]=1.0			,							UpperCamelCase__:				Dict=False			,							UpperCamelCase__:				Dict="sine"			,							UpperCamelCase__:				str="resnet50"			,							UpperCamelCase__:				List[str]=True			,							UpperCamelCase__:				Tuple=False			,							UpperCamelCase__:				List[str]=1			,							UpperCamelCase__:				Tuple=5			,							UpperCamelCase__:				Any=2			,							UpperCamelCase__:				Optional[int]=1			,							UpperCamelCase__:				Tuple=1			,							UpperCamelCase__:				List[Any]=5			,							UpperCamelCase__:				Optional[int]=2			,							UpperCamelCase__:				int=0.1			,							**UpperCamelCase__:				Optional[int]			,							):
														if backbone_config is not None and use_timm_backbone:
																					raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""     )

														if not use_timm_backbone:
																					if backbone_config is None:
																												logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""     )
																												lowerCamelCase__					:	str         =       CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]     )
																					elif isinstance(UpperCamelCase__			,							UpperCamelCase__     ):
																												lowerCamelCase__					:	Optional[int]         =       backbone_config.get("""model_type"""     )
																												lowerCamelCase__					:	Tuple         =       CONFIG_MAPPING[backbone_model_type]
																												lowerCamelCase__					:	str         =       config_class.from_dict(UpperCamelCase__     )
																					# set timm attributes to None
																					lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	Optional[int]         =       None, None, None

														lowerCamelCase__					:	Union[str, Any]         =       use_timm_backbone
														lowerCamelCase__					:	Optional[Any]         =       backbone_config
														lowerCamelCase__					:	Any         =       num_channels
														lowerCamelCase__					:	Tuple         =       num_queries
														lowerCamelCase__					:	Dict         =       d_model
														lowerCamelCase__					:	List[Any]         =       encoder_ffn_dim
														lowerCamelCase__					:	int         =       encoder_layers
														lowerCamelCase__					:	Union[str, Any]         =       encoder_attention_heads
														lowerCamelCase__					:	Dict         =       decoder_ffn_dim
														lowerCamelCase__					:	List[Any]         =       decoder_layers
														lowerCamelCase__					:	Any         =       decoder_attention_heads
														lowerCamelCase__					:	Any         =       dropout
														lowerCamelCase__					:	List[Any]         =       attention_dropout
														lowerCamelCase__					:	List[str]         =       activation_dropout
														lowerCamelCase__					:	List[Any]         =       activation_function
														lowerCamelCase__					:	Tuple         =       init_std
														lowerCamelCase__					:	List[str]         =       init_xavier_std
														lowerCamelCase__					:	Optional[int]         =       encoder_layerdrop
														lowerCamelCase__					:	Any         =       decoder_layerdrop
														lowerCamelCase__					:	int         =       encoder_layers
														lowerCamelCase__					:	Tuple         =       auxiliary_loss
														lowerCamelCase__					:	Tuple         =       position_embedding_type
														lowerCamelCase__					:	List[Any]         =       backbone
														lowerCamelCase__					:	Optional[Any]         =       use_pretrained_backbone
														lowerCamelCase__					:	Any         =       dilation
														# Hungarian matcher
														lowerCamelCase__					:	Dict         =       class_cost
														lowerCamelCase__					:	Union[str, Any]         =       bbox_cost
														lowerCamelCase__					:	Any         =       giou_cost
														# Loss coefficients
														lowerCamelCase__					:	Any         =       mask_loss_coefficient
														lowerCamelCase__					:	Dict         =       dice_loss_coefficient
														lowerCamelCase__					:	Union[str, Any]         =       bbox_loss_coefficient
														lowerCamelCase__					:	List[Any]         =       giou_loss_coefficient
														lowerCamelCase__					:	List[str]         =       eos_coefficient
														super().__init__(is_encoder_decoder=UpperCamelCase__			,							**UpperCamelCase__     )




							@property
							def 		lowerCamelCase_				(							self:				List[Any]     ):
														return self.encoder_attention_heads




							@property
							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														return self.d_model



class _lowercase	(				_lowercase		):
							a				      =       version.parse("""1.11"""		)




							@property
							def 		lowerCamelCase_				(							self:				Any     ):
														return OrderedDict(
														    [
														        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
														        ("""pixel_mask""", {0: """batch"""}),
														    ]     )




							@property
							def 		lowerCamelCase_				(							self:				List[Any]     ):
														return 1e-5




							@property
							def 		lowerCamelCase_				(							self:				Tuple     ):
														return 12





'''simple docstring'''



#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist






def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   int   ):
					with open(_SCREAMING_SNAKE_CASE						,      'r'   ) as fh:
										fcntl.flock(_SCREAMING_SNAKE_CASE						,      fcntl.LOCK_EX   )
										try:
															print(*_SCREAMING_SNAKE_CASE   )
										finally:
															fcntl.flock(_SCREAMING_SNAKE_CASE						,      fcntl.LOCK_UN   )


__lowercase	:						Dict       =	int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
__lowercase	:						Tuple       =	torch.device('cuda', local_rank)
__lowercase	:						Optional[int]       =	socket.gethostname()

__lowercase	:						List[str]       =	f'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	__lowercase	:						str       =	dist.get_rank()
	__lowercase	:						Union[str, Any]       =	dist.get_world_size()

	printflock(f'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(f'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(f'''{gpu} is broken''')
	raise



