



#
# 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						(       *__UpperCamelCase					)						->      Union[str, Any]:
			with open(__UpperCamelCase ,	'r'					) as fh:
						fcntl.flock(__UpperCamelCase ,	fcntl.LOCK_EX					)
						try:
									print(*__UpperCamelCase					)
						finally:
									fcntl.flock(__UpperCamelCase ,	fcntl.LOCK_UN					)


SCREAMING_SNAKE_CASE__       =      int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
SCREAMING_SNAKE_CASE__       =      torch.device('''cuda''', local_rank)
SCREAMING_SNAKE_CASE__       =      socket.gethostname()

SCREAMING_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
							SCREAMING_SNAKE_CASE__       =      dist.get_rank()
							SCREAMING_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









import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
							import optax
							from flax.training.common_utils import onehot

							from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
							from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'					)
								A__       =    AutoTokenizer.from_pretrained('google/mt5-small'					)

								A__       =    tokenizer('Hello there'				,					return_tensors='np'					).input_ids
								A__       =    tokenizer('Hi I am'				,					return_tensors='np'					).input_ids

								A__       =    shift_tokens_right(_snake_case				,					model.config.pad_token_id				,					model.config.decoder_start_token_id					)

								A__       =    model(_snake_case				,					decoder_input_ids=_snake_case					).logits
								A__       =    optax.softmax_cross_entropy(_snake_case				,					onehot(_snake_case				,					logits.shape[-1]					)					).mean()

								A__       =    -(labels.shape[-1] * loss.item())

								A__       =    -84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




