

# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
    import jax
    import jaxlib

__A			:					List[str]						=			get_logger()

__A			:					Optional[dict]						=			None





class 			_SCREAMING_SNAKE_CASE (   TensorFormatter[Mapping, "jax.Array", Mapping]		):







   '''simple docstring'''




   def __init__(       self					:			Tuple     , __lowerCamelCase					:			Tuple=None     , __lowerCamelCase					:			int=None     , **__lowerCamelCase					:			Dict			):
       super().__init__(features=__lowerCamelCase			)
       import jax
       from jaxlib.xla_client import Device

       if isinstance(__lowerCamelCase     , __lowerCamelCase			):
           raise ValueError(
               f"Expected {device} to be a `str` not {type(__lowerCamelCase			)}, as `jaxlib.xla_extension.Device` "
               "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
               "the device with `str()` to get its string identifier that will be internally mapped "
               "to the actual `jaxlib.xla_extension.Device`."			)
       SCREAMING_SNAKE_CASE										=   device if isinstance(__lowerCamelCase     , __lowerCamelCase			) else str(jax.devices()[0]			)
       # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
       # with `pickle` nor with `dill`, so we need to use a global variable instead
       global DEVICE_MAPPING
       if DEVICE_MAPPING is None:
           SCREAMING_SNAKE_CASE										=   self._map_devices_to_str()
       if self.device not in list(DEVICE_MAPPING.keys()			):
           logger.warning(
               f"Device with string identifier {self.device} not listed among the available "
               f"devices: {list(DEVICE_MAPPING.keys()			)}, so falling back to the default "
               f"device: {str(jax.devices()[0]			)}."			)
           SCREAMING_SNAKE_CASE										=   str(jax.devices()[0]			)
       SCREAMING_SNAKE_CASE										=   jnp_array_kwargs





   @staticmethod
   def      _snake_case		(       ):
       import jax

       return {str(__lowerCamelCase			): device for device in jax.devices()}





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			List[Any]			):
       import jax
       import jax.numpy as jnp

       if isinstance(__lowerCamelCase     , __lowerCamelCase			) and column:
           if all(
               isinstance(__lowerCamelCase     , jax.Array			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
               return jnp.stack(__lowerCamelCase     , axis=0			)
       return column





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			Dict			):
       import jax
       import jax.numpy as jnp

       if isinstance(__lowerCamelCase     , (str, bytes, type(__lowerCamelCase			))			):
           return value
       elif isinstance(__lowerCamelCase     , (np.character, np.ndarray)			) and np.issubdtype(value.dtype     , np.character			):
           return value.tolist()

       SCREAMING_SNAKE_CASE										=   {}

       if isinstance(__lowerCamelCase     , (np.number, np.ndarray)			) and np.issubdtype(value.dtype     , np.integer			):
           # the default int precision depends on the jax config
           # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
           if jax.config.jax_enable_xaa:
               SCREAMING_SNAKE_CASE										=   {"dtype": jnp.intaa}
           else:
               SCREAMING_SNAKE_CASE										=   {"dtype": jnp.intaa}
       elif isinstance(__lowerCamelCase     , (np.number, np.ndarray)			) and np.issubdtype(value.dtype     , np.floating			):
           SCREAMING_SNAKE_CASE										=   {"dtype": jnp.floataa}
       elif config.PIL_AVAILABLE and "PIL" in sys.modules:
           import PIL.Image

           if isinstance(__lowerCamelCase     , PIL.Image.Image			):
               SCREAMING_SNAKE_CASE										=   np.asarray(__lowerCamelCase			)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
       global DEVICE_MAPPING
       if DEVICE_MAPPING is None:
           SCREAMING_SNAKE_CASE										=   self._map_devices_to_str()

       with jax.default_device(DEVICE_MAPPING[self.device]			):
           # calling jnp.array on a np.ndarray does copy the data
           # see https://github.com/google/jax/issues/4486
           return jnp.array(__lowerCamelCase     , **{**default_dtype, **self.jnp_array_kwargs}			)





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			int			):
       import jax

       # support for torch, tf, jax etc.
       if config.TORCH_AVAILABLE and "torch" in sys.modules:
           import torch

           if isinstance(__lowerCamelCase     , torch.Tensor			):
               return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
       if hasattr(__lowerCamelCase     , "__array__"			) and not isinstance(__lowerCamelCase     , jax.Array			):
           SCREAMING_SNAKE_CASE										=   data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(__lowerCamelCase     , np.ndarray			):
           if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
               return self._consolidate([self.recursive_tensorize(__lowerCamelCase			) for substruct in data_struct]			)
       elif isinstance(__lowerCamelCase     , (list, tuple)			):
           return self._consolidate([self.recursive_tensorize(__lowerCamelCase			) for substruct in data_struct]			)
       return self._tensorize(__lowerCamelCase			)





   def      _snake_case		(       self					:			Union[str, Any]     , __lowerCamelCase					:			dict			):
       return map_nested(self._recursive_tensorize     , __lowerCamelCase     , map_list=__lowerCamelCase			)





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			pa.Table			):
       SCREAMING_SNAKE_CASE										=   self.numpy_arrow_extractor().extract_row(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.python_features_decoder.decode_row(__lowerCamelCase			)
       return self.recursive_tensorize(__lowerCamelCase			)





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			pa.Table			):
       SCREAMING_SNAKE_CASE										=   self.numpy_arrow_extractor().extract_column(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.python_features_decoder.decode_column(__lowerCamelCase     , pa_table.column_names[0]			)
       SCREAMING_SNAKE_CASE										=   self.recursive_tensorize(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self._consolidate(__lowerCamelCase			)
       return column





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			pa.Table			):
       SCREAMING_SNAKE_CASE										=   self.numpy_arrow_extractor().extract_batch(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.python_features_decoder.decode_batch(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.recursive_tensorize(__lowerCamelCase			)
       for column_name in batch:
           SCREAMING_SNAKE_CASE										=   self._consolidate(batch[column_name]			)
       return batch



#
# 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


