'''simple docstring'''




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__SCREAMING_SNAKE_CASE       :     Any     =  logging.get_logger(__name__)  # pylint: disable=invalid-name

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    Dict:


       """simple docstring"""







       if not path:
              return "pipe"

       for ext in PipelineDataFormat.SUPPORTED_FORMATS:
              if path.endswith(_UpperCAmelCase    ):
                     return ext

       raise Exception(
           F"""Unable to determine file format from file extension {path}. """
           F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    Tuple:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[Any]							      =    pipeline(
           task=args.task			,	model=args.model if args.model else None			,	config=args.config			,	tokenizer=args.tokenizer			,	device=args.device			,	)
       _UpperCAmelCase     :  Optional[Any]							      =    try_infer_format_from_ext(args.input    ) if args.format == "infer" else args.format
       _UpperCAmelCase     :  List[Any]							      =    PipelineDataFormat.from_str(
           format=_UpperCAmelCase			,	output_path=args.output			,	input_path=args.input			,	column=args.column if args.column else nlp.default_input_names			,	overwrite=args.overwrite			,	)
       return RunCommand(_UpperCAmelCase			,	_UpperCAmelCase    )


class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				str						,					A						:				Pipeline						,					A						:				PipelineDataFormat       ):
             _UpperCAmelCase     :  Any							      =    nlp
             _UpperCAmelCase     :  List[Any]							      =    reader




      @staticmethod
      def      _A	(       A						:				ArgumentParser       ):
             _UpperCAmelCase     :  List[str]							      =    parser.add_parser("run"						,					help="Run a pipeline through the CLI"       )
             run_parser.add_argument("--task"						,					choices=get_supported_tasks()						,					help="Task to run"       )
             run_parser.add_argument("--input"						,					type=A						,					help="Path to the file to use for inference"       )
             run_parser.add_argument("--output"						,					type=A						,					help="Path to the file that will be used post to write results."       )
             run_parser.add_argument("--model"						,					type=A						,					help="Name or path to the model to instantiate."       )
             run_parser.add_argument("--config"						,					type=A						,					help="Name or path to the model's config to instantiate."       )
             run_parser.add_argument(
                 "--tokenizer"						,					type=A						,					help="Name of the tokenizer to use. (default: same as the model name)"       )
             run_parser.add_argument(
                 "--column"						,					type=A						,					help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"						,					)
             run_parser.add_argument(
                 "--format"						,					type=A						,					default="infer"						,					choices=PipelineDataFormat.SUPPORTED_FORMATS						,					help="Input format to read from"						,					)
             run_parser.add_argument(
                 "--device"						,					type=A						,					default=-1						,					help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"						,					)
             run_parser.add_argument("--overwrite"						,					action="store_true"						,					help="Allow overwriting the output file."       )
             run_parser.set_defaults(func=A       )





      def      _A	(       self						:				int       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    self._nlp, []

             for entry in self._reader:
                    _UpperCAmelCase     :  Optional[int]							      =    nlp(**A       ) if self._reader.is_multi_columns else nlp(A       )
                    if isinstance(A						,					A       ):
                           outputs.append(A       )
                    else:
                           outputs += output

        # Saving data
             if self._nlp.binary_output:
                    _UpperCAmelCase     :  Dict							      =    self._reader.save_binary(A       )
                    logger.warning(F"""Current pipeline requires output to be in binary format, saving at {binary_path}"""       )
             else:
                    self._reader.save(A       )





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


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

lowerCamelCase__					=   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
						lowerCamelCase__					=   dist.get_rank()
						lowerCamelCase__					=   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



