




import os
import sys
import unittest


_lowerCamelCase	:		List[Any]							       =   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_lowerCamelCase	:		List[Any]							       =   os.path.join(git_repo_path, """src""", """transformers""")

_lowerCamelCase	:		Optional[Any]							       =   """\n{0} = None\n"""

_lowerCamelCase	:		List[Any]							       =   """\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"""


_lowerCamelCase	:		Union[str, Any]							       =   """\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"""


class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->str:





           '''simple docstring'''

           A__       =    find_backend('''    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")''')
           self.assertIsNone(lowerCamelCase__)

           A__       =    find_backend('''    if not is_tokenizers_available():''')
           self.assertEqual(lowerCamelCase__					, '''tokenizers''')

           A__       =    find_backend('''    if not is_tensorflow_text_available():''')
           self.assertEqual(lowerCamelCase__					, '''tensorflow_text''')

           A__       =    find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):''')
           self.assertEqual(lowerCamelCase__					, '''sentencepiece_and_tokenizers''')

           A__       =    find_backend(
               '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):''')
           self.assertEqual(lowerCamelCase__					, '''sentencepiece_and_tensorflow_text''')

           A__       =    find_backend(
               '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):''')
           self.assertEqual(lowerCamelCase__					, '''sentencepiece_and_tokenizers_and_vision''')


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Tuple:





           '''simple docstring'''

           A__       =    read_init()
           # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
           self.assertIn('''torch'''					, lowerCamelCase__)
           self.assertIn('''tensorflow_text'''					, lowerCamelCase__)
           self.assertIn('''sentencepiece_and_tokenizers'''					, lowerCamelCase__)

           # Likewise, we can't assert on the exact content of a key
           self.assertIn('''BertModel'''					, objects['''torch'''])
           self.assertIn('''TFBertModel'''					, objects['''tf'''])
           self.assertIn('''FlaxBertModel'''					, objects['''flax'''])
           self.assertIn('''BertModel'''					, objects['''torch'''])
           self.assertIn('''TFBertTokenizer'''					, objects['''tensorflow_text'''])
           self.assertIn('''convert_slow_tokenizer'''					, objects['''sentencepiece_and_tokenizers'''])


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





           '''simple docstring'''

           A__       =    create_dummy_object('''CONSTANT'''					, '''\'torch\'''')
           self.assertEqual(lowerCamelCase__					, '''\nCONSTANT = None\n''')

           A__       =    create_dummy_object('''function'''					, '''\'torch\'''')
           self.assertEqual(
               lowerCamelCase__					, '''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n''')

           A__       =    "\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n"
           A__       =    create_dummy_object('''FakeClass'''					, '''\'torch\'''')
           self.assertEqual(lowerCamelCase__					, lowerCamelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->List[str]:





           '''simple docstring'''

           A__       =    "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n"
           A__       =    create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']})
           self.assertEqual(dummy_files['''torch''']					, lowerCamelCase__)






'''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



