


'''simple docstring'''




import os
import sys
import unittest


__snake_case       		=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
__snake_case       		=os.path.join(git_repo_path, """src""", """diffusers""")

class 						UpperCAmelCase_ (    unittest.TestCase						):





     def 				__UpperCAmelCase (					self			: Union[str, Any]       )							->    Any:
        lowerCAmelCase	=     find_backend('    if not is_torch_available():'       )
        self.assertEqual(A_       , 'torch'       )

        # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
        # self.assertEqual(backend_with_underscore, "tensorflow_text")

        lowerCAmelCase	=     find_backend('    if not (is_torch_available() and is_transformers_available()):'       )
        self.assertEqual(A_       , 'torch_and_transformers'       )

        # double_backend_with_underscore = find_backend(
        #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
        # )
        # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

        lowerCAmelCase	=     find_backend(
            '    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'       )
        self.assertEqual(A_       , 'torch_and_transformers_and_onnx'       )





     def 				__UpperCAmelCase (					self			: Optional[int]       )							->    List[str]:
        lowerCAmelCase	=     read_init()
        # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
        self.assertIn('torch'       , A_       )
        self.assertIn('torch_and_transformers'       , A_       )
        self.assertIn('flax_and_transformers'       , A_       )
        self.assertIn('torch_and_transformers_and_onnx'       , A_       )

        # Likewise, we can't assert on the exact content of a key
        self.assertIn('UNet2DModel'       , objects['torch']       )
        self.assertIn('FlaxUNet2DConditionModel'       , objects['flax']       )
        self.assertIn('StableDiffusionPipeline'       , objects['torch_and_transformers']       )
        self.assertIn('FlaxStableDiffusionPipeline'       , objects['flax_and_transformers']       )
        self.assertIn('LMSDiscreteScheduler'       , objects['torch_and_scipy']       )
        self.assertIn('OnnxStableDiffusionPipeline'       , objects['torch_and_transformers_and_onnx']       )





     def 				__UpperCAmelCase (					self			: Optional[int]       )							->    Tuple:
        lowerCAmelCase	=     create_dummy_object('CONSTANT'       , '\'torch\''       )
        self.assertEqual(A_       , '\nCONSTANT = None\n'       )

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

        lowerCAmelCase	=     '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n'
        lowerCAmelCase	=     create_dummy_object('FakeClass'       , '\'torch\''       )
        self.assertEqual(A_       , A_       )





     def 				__UpperCAmelCase (					self			: List[str]       )							->    List[str]:
        lowerCAmelCase	=     '# 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\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n'
        lowerCAmelCase	=     create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}       )
        self.assertEqual(dummy_files['torch']       , A_       )









#
# 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


