


'''simple docstring'''




import math
import sys





def       a_   (					lowerCamelCase      :	List[Any]							):
   lowerCAmelCase	=     ''
   try:
      with open(_lowercase      ,					'rb'							) as binary_file:
         lowerCAmelCase	=     binary_file.read()
      for dat in data:
         lowerCAmelCase	=     f'''{dat:08b}'''
         result += curr_byte
      return result
   except OSError:
      print('File not accessible'							)
      sys.exit()





def       a_   (					lowerCamelCase      :	Tuple							):
   lowerCAmelCase	=     {'0': '0', '1': '1'}
   lowerCAmelCase	,    lowerCAmelCase	=     '', ''
   lowerCAmelCase	=     len(_lowercase							)

   for i in range(len(_lowercase							)							):
      curr_string += data_bits[i]
      if curr_string not in lexicon:
         continue

      lowerCAmelCase	=     lexicon[curr_string]
      result += last_match_id
      lowerCAmelCase	=     last_match_id + '0'

      if math.loga(_lowercase							).is_integer():
         lowerCAmelCase	=     {}
         for curr_key in list(_lowercase							):
            lowerCAmelCase	=     lexicon.pop(_lowercase							)
         lowerCAmelCase	=     new_lex

      lowerCAmelCase	=     last_match_id + '1'
      index += 1
      lowerCAmelCase	=     ''
   return result





def       a_   (					lowerCamelCase      :	List[Any]      ,					lowerCamelCase      :	Optional[int]							):
   lowerCAmelCase	=     8
   try:
      with open(_lowercase      ,					'wb'							) as opened_file:
         lowerCAmelCase	=     [
             to_write[i : i + byte_length]
             for i in range(0      ,					len(_lowercase							)      ,					_lowercase							)
         ]

         if len(result_byte_array[-1]							) % byte_length == 0:
            result_byte_array.append('10000000'							)
         else:
            result_byte_array[-1] += "1" + "0" * (
                byte_length - len(result_byte_array[-1]							) - 1
            )

         for elem in result_byte_array[:-1]:
            opened_file.write(int(_lowercase      ,					2							).to_bytes(1      ,					byteorder='big'							)							)
   except OSError:
      print('File not accessible'							)
      sys.exit()





def       a_   (					lowerCamelCase      :	Any							):
   lowerCAmelCase	=     0
   for letter in data_bits:
      if letter == "1":
         break
      counter += 1

   lowerCAmelCase	=     data_bits[counter:]
   lowerCAmelCase	=     data_bits[counter + 1 :]
   return data_bits





def       a_   (					lowerCamelCase      :	Tuple      ,					lowerCamelCase      :	int							):
   lowerCAmelCase	=     read_file_binary(_lowercase							)
   lowerCAmelCase	=     remove_prefix(_lowercase							)
   lowerCAmelCase	=     decompress_data(_lowercase							)
   write_file_binary(_lowercase      ,					_lowercase							)


if __name__ == "__main__":
       compress(sys.argv[1], sys.argv[2])









#
# 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


