
import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()





class   __UpperCamelCase      (				__SCREAMING_SNAKE_CASE      ,		__SCREAMING_SNAKE_CASE      ,		unittest.TestCase    ):






     """simple docstring"""







     lowerCAmelCase_									=       VQModel
     lowerCAmelCase_									=       '''sample'''



     @property
     def       UpperCAmelCase__				(							self				:				List[Any]			,		_A				:				Optional[int]=(32, 32)      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     Dict =						4
       __SCREAMING_SNAKE_CASE	:     Dict =						3

       __SCREAMING_SNAKE_CASE	:     int =						floats_tensor((batch_size, num_channels) + sizes      ).to(_SCREAMING_SNAKE_CASE      )

       return {"sample": image}



     @property
     def       UpperCAmelCase__				(							self				:				Optional[Any]      ):







       """simple docstring"""




       return (3, 32, 32)



     @property
     def       UpperCAmelCase__				(							self				:				Tuple      ):







       """simple docstring"""




       return (3, 32, 32)



     def       UpperCAmelCase__				(							self				:				Optional[Any]      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     List[Any] =						{
           "block_out_channels": [32, 64],
           "in_channels": 3,
           "out_channels": 3,
           "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
           "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
           "latent_channels": 3,
       }
       __SCREAMING_SNAKE_CASE	:     Tuple =						self.dummy_input
       return init_dict, inputs_dict



     def       UpperCAmelCase__				(							self				:				Dict      ):







       """simple docstring"""




       pass



     def       UpperCAmelCase__				(							self				:				Union[str, Any]      ):







       """simple docstring"""




       pass



     def       UpperCAmelCase__				(							self				:				Tuple      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     str =						VQModel.from_pretrained('''fusing/vqgan-dummy'''			,		output_loading_info=_SCREAMING_SNAKE_CASE      )
       self.assertIsNotNone(_SCREAMING_SNAKE_CASE      )
       self.assertEqual(len(loading_info['''missing_keys''']      )			,		0      )

       model.to(_SCREAMING_SNAKE_CASE      )
       __SCREAMING_SNAKE_CASE	:     List[Any] =						model(**self.dummy_input      )

       assert image is not None, "Make sure output is not None"




     def       UpperCAmelCase__				(							self				:				Optional[Any]      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						VQModel.from_pretrained('''fusing/vqgan-dummy'''      )
       model.to(_SCREAMING_SNAKE_CASE      ).eval()

       torch.manual_seed(0      )
       if torch.cuda.is_available():
         torch.cuda.manual_seed_all(0      )

       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						torch.randn(1			,		model.config.in_channels			,		model.config.sample_size			,		model.config.sample_size      )
       __SCREAMING_SNAKE_CASE	:     int =						image.to(_SCREAMING_SNAKE_CASE      )
       with torch.no_grad():
         __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						model(_SCREAMING_SNAKE_CASE      ).sample

       __SCREAMING_SNAKE_CASE	:     Optional[int] =						output[0, -1, -3:, -3:].flatten().cpu()
       # fmt: off
       __SCREAMING_SNAKE_CASE	:     Tuple =						torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43]      )
       # fmt: on
       self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		atol=1e-3      )      )





"""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 			_lowercase				(  *__snake_case	)						->   Optional[Any]:
			with open(__snake_case					,"r"	) as fh:
						fcntl.flock(__snake_case					,fcntl.LOCK_EX	)
						try:
									print(*__snake_case	)
						finally:
									fcntl.flock(__snake_case					,fcntl.LOCK_UN	)


__snake_case							:   List[Any]      =    int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
__snake_case							:   List[str]      =    torch.device('cuda', local_rank)
__snake_case							:   Optional[Any]      =    socket.gethostname()

__snake_case							:   str      =    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							:   Tuple      =    dist.get_rank()
	__snake_case							:   Any      =    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