import copy
from typing import Dict, List, Optional

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


UpperCAmelCase_								=							{
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

UpperCAmelCase_								=							logging.get_logger(__name__)





class      lowerCAmelCase						(					__lowerCamelCase			):
     _SCREAMING_SNAKE_CASE     :     Optional[Any]		="""mask2former"""
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=["""swin"""]
     _SCREAMING_SNAKE_CASE     :     List[Any]		={"""hidden_size""": """hidden_dim"""}



     def __init__(					self				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = 256				,      lowerCAmelCase__ = 256				,      lowerCAmelCase__ = 256				,      lowerCAmelCase__ = 1024				,      lowerCAmelCase__ = "relu"				,      lowerCAmelCase__ = 6				,      lowerCAmelCase__ = 10				,      lowerCAmelCase__ = 8				,      lowerCAmelCase__ = 0.0				,      lowerCAmelCase__ = 2048				,      lowerCAmelCase__ = False				,      lowerCAmelCase__ = False				,      lowerCAmelCase__ = 4				,      lowerCAmelCase__ = 255				,      lowerCAmelCase__ = 100				,      lowerCAmelCase__ = 0.1				,      lowerCAmelCase__ = 2.0				,      lowerCAmelCase__ = 5.0				,      lowerCAmelCase__ = 5.0				,      lowerCAmelCase__ = 12544				,      lowerCAmelCase__ = 3.0				,      lowerCAmelCase__ = 0.75				,      lowerCAmelCase__ = 0.02				,      lowerCAmelCase__ = 1.0				,      lowerCAmelCase__ = True				,      lowerCAmelCase__ = [4, 8, 16, 32]				,      lowerCAmelCase__ = None				,      **lowerCAmelCase__				,      ):
            if backbone_config is None:
                   logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'       )
                   _A=	CONFIG_MAPPING['swin'](
                       image_size=224				,      in_channels=3				,      patch_size=4				,      embed_dim=96				,      depths=[2, 2, 18, 2]				,      num_heads=[3, 6, 12, 24]				,      window_size=7				,      drop_path_rate=0.3				,      use_absolute_embeddings=UpperCAmelCase_				,      out_features=['stage1', 'stage2', 'stage3', 'stage4']				,      )

            if isinstance(UpperCAmelCase_				,      UpperCAmelCase_       ):
                   _A=	backbone_config.pop('model_type'       )
                   _A=	CONFIG_MAPPING[backbone_model_type]
                   _A=	config_class.from_dict(UpperCAmelCase_       )

            # verify that the backbone is supported
            if backbone_config.model_type not in self.backbones_supported:
                   logger.warning_once(
                       f"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
                       f"Supported model types: {','.join(self.backbones_supported       )}"       )

            _A=	backbone_config
            _A=	feature_size
            _A=	mask_feature_size
            _A=	hidden_dim
            _A=	encoder_feedforward_dim
            _A=	activation_function
            _A=	encoder_layers
            _A=	decoder_layers
            _A=	num_attention_heads
            _A=	dropout
            _A=	dim_feedforward
            _A=	pre_norm
            _A=	enforce_input_projection
            _A=	common_stride
            _A=	ignore_value
            _A=	num_queries
            _A=	no_object_weight
            _A=	class_weight
            _A=	mask_weight
            _A=	dice_weight
            _A=	train_num_points
            _A=	oversample_ratio
            _A=	importance_sample_ratio
            _A=	init_std
            _A=	init_xavier_std
            _A=	use_auxiliary_loss
            _A=	feature_strides
            _A=	output_auxiliary_logits
            _A=	decoder_layers

            super().__init__(**UpperCAmelCase_       )



     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            return cls(
                backbone_config=UpperCAmelCase_				,      **UpperCAmelCase_				,      )





     def 			a__  (					self       ):
            _A=	copy.deepcopy(self.__dict__       )
            _A=	self.backbone_config.to_dict()
            _A=	self.__class__.model_type
            return output







#
# 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 							UpperCamelCase				(    *lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






       with open(lowerCAmelCase_       ,					'r'   ) as fh:
              fcntl.flock(lowerCAmelCase_       ,					fcntl.LOCK_EX   )
              try:
                     print(*lowerCAmelCase_   )
              finally:
                     fcntl.flock(lowerCAmelCase_       ,					fcntl.LOCK_UN   )


UpperCAmelCase_								=							int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
UpperCAmelCase_								=							torch.device('''cuda''', local_rank)
UpperCAmelCase_								=							socket.gethostname()

UpperCAmelCase_								=							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
      UpperCAmelCase_								=							dist.get_rank()
      UpperCAmelCase_								=							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