


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 	__lowerCamelCase			(_a						):
       _lowercase											=     ["""image_processor""", """tokenizer"""]
       _lowercase											=     """AutoImageProcessor"""
       _lowercase											=     """AutoTokenizer"""







       def __init__(			self: int,A_: Any,A_: Tuple					):






            '''simple docstring'''




            super().__init__(A_,A_					)
            __UpperCamelCase =			self.image_processor







       def __call__(			self: str,A_: Union[str, Any]=None,A_: Optional[int]=None,A_: Optional[int]=None,**A_: Union[str, Any]					):






            '''simple docstring'''




            if text is None and images is None:
                 raise ValueError('You have to specify either text or images. Both cannot be none.'					)

            if text is not None:
                 __UpperCamelCase =			self.tokenizer(A_,return_tensors=A_,**A_					)

            if images is not None:
                 __UpperCamelCase =			self.image_processor(A_,return_tensors=A_,**A_					)

            if text is not None and images is not None:
                 __UpperCamelCase =			image_features.pixel_values
                 return encoding
            elif text is not None:
                 return encoding
            else:
                 return BatchEncoding(data=dict(**A_					),tensor_type=A_					)







       def 		snake_case_	(			self: Dict,*A_: Optional[Any],**A_: Optional[int]					):






            '''simple docstring'''




            return self.tokenizer.batch_decode(*A_,**A_					)







       def 		snake_case_	(			self: int,*A_: Union[str, Any],**A_: Tuple					):






            '''simple docstring'''




            return self.tokenizer.decode(*A_,**A_					)







       @property
       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            return ["input_ids", "attention_mask", "pixel_values"]






#
# 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 		_A		(		*_lowercase )					-> Tuple:


     """simple docstring"""


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


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

__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
    __snake_case   							=						dist.get_rank()
    __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


