





import fcntl
import os
import socket

import torch
import torch.distributed as dist
def 					_A	(		*SCREAMING_SNAKE_CASE					:       str						):





  """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     :     List[str]							=	int(os.environ["""LOCAL_RANK"""])
torch.cuda.set_device(local_rank)
UpperCAmelCase     :     int							=	torch.device("""cuda""", local_rank)
UpperCAmelCase     :     Union[str, Any]							=	socket.gethostname()

UpperCAmelCase     :     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
     UpperCAmelCase     :     Dict							=	dist.get_rank()
     UpperCAmelCase     :     List[str]							=	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













import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate







@dataclass(frozen=UpperCamelCase__)
class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : str            =      field(default="""image-classification"""						,    metadata={"""include_in_asdict_even_if_is_default""": True})
      _lowercase    : ClassVar[Features]            =      Features({"""image""": Image()})
      _lowercase    : ClassVar[Features]            =      Features({"""labels""": ClassLabel})
      _lowercase    : str            =      "image"
      _lowercase    : str            =      "labels"







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						List[Any]:



        '''simple docstring'''




        if self.label_column not in features:
          raise ValueError(F'''Column {self.label_column} is not present in features.'''							)
        if not isinstance(features[self.label_column]		,				lowerCAmelCase__							):
          raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''							)
        a__     : int  =copy.deepcopy(self							)
        a__     : Dict  =self.label_schema.copy()
        a__     : int  =features[self.label_column]
        a__     : str  =label_schema
        return task_template







      @property
      def        _lowercase  (   self							)       ->						Dict[str, str]:



        '''simple docstring'''




        return {
            self.image_column: "image",
            self.label_column: "labels",
        }






