






#
# 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	(						*__A    :							Tuple )      ->					str:


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


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

SCREAMING_SNAKE_CASE 							=						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
  SCREAMING_SNAKE_CASE 							=						dist.get_rank()
  SCREAMING_SNAKE_CASE 							=						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 unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE 							=						'\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'
class 							A_    (						unittest.TestCase ,   __lowercase			):






 '''simple docstring'''




 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				load_tool('''text-question-answering''')
     self.tool.setup()
     _UpperCAmelCase				:      Any			  =				load_tool('''text-question-answering'''							,  remote=_A)


 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				self.tool(_A							,  '''What did Hugging Face do in April 2021?''')
     self.assertEqual(_A							,  '''launched the BigScience Research Workshop''')


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				self.remote_tool(_A							,  '''What did Hugging Face do in April 2021?''')
     self.assertEqual(_A							,  '''launched the BigScience Research Workshop''')


 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.tool(text=_A							,  question='''What did Hugging Face do in April 2021?''')
     self.assertEqual(_A							,  '''launched the BigScience Research Workshop''')


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				self.remote_tool(text=_A							,  question='''What did Hugging Face do in April 2021?''')
     self.assertEqual(_A							,  '''launched the BigScience Research Workshop''')




