


'''simple docstring'''


import os
import sys
import unittest


lowerCamelCase__					=   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__					=   os.path.join(git_repo_path, 'src', 'diffusers')






class 		lowerCAmelCase__	(	unittest.TestCase		):




			def 							lowerCAmelCase__			(						self     :   Tuple       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					find_backend("    if not is_torch_available():"       )
					self.assertEqual(lowerCamelCase__		,     "torch"       )

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

					_UpperCAmelCase						:				List[Any]	    =					find_backend("    if not (is_torch_available() and is_transformers_available()):"       )
					self.assertEqual(lowerCamelCase__		,     "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")

					_UpperCAmelCase						:				Tuple	    =					find_backend(
					    "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"       )
					self.assertEqual(lowerCamelCase__		,     "torch_and_transformers_and_onnx"       )




			def 							lowerCAmelCase__			(						self     :   Tuple       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					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("torch_and_transformers"		,     lowerCamelCase__       )
					self.assertIn("flax_and_transformers"		,     lowerCamelCase__       )
					self.assertIn("torch_and_transformers_and_onnx"		,     lowerCamelCase__       )

					# 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 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					create_dummy_object("CONSTANT"		,     "'torch'"       )
					self.assertEqual(lowerCamelCase__		,     "\nCONSTANT = None\n"       )

					_UpperCAmelCase						:				Union[str, Any]	    =					create_dummy_object("function"		,     "'torch'"       )
					self.assertEqual(
					    lowerCamelCase__		,     "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"       )

					_UpperCAmelCase						:				Optional[int]	    =					"\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"
					_UpperCAmelCase						:				int	    =					create_dummy_object("FakeClass"		,     "'torch'"       )
					self.assertEqual(lowerCamelCase__		,     lowerCamelCase__       )






			def 							lowerCAmelCase__			(						self     :   Any       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					"# 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"
					_UpperCAmelCase						:				Tuple	    =					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



