




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_A     =			version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
						from nltk import word_tokenize


_A     =			'''\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n'''

_A     =			'''\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'''

_A     =			'''\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n'''


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,				_KWARGS_DESCRIPTION     )
class  A		(  datasets.Metric     ):



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return datasets.MetricInfo(
								    description=_DESCRIPTION,	citation=_CITATION,	inputs_description=_KWARGS_DESCRIPTION,	features=datasets.Features(
								        {
								            '''predictions''': datasets.Value('''string''',	id='''sequence'''   ),
								            '''references''': datasets.Value('''string''',	id='''sequence'''   ),
								        }   ),	codebase_urls=['''https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'''],	reference_urls=[
								        '''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
								        '''https://en.wikipedia.org/wiki/METEOR''',
								    ],	)



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								import nltk

								nltk.download('''wordnet'''   )
								if NLTK_VERSION >= version.Version('''3.6.5'''   ):
														nltk.download('''punkt'''   )
								if NLTK_VERSION >= version.Version('''3.6.6'''   ):
														nltk.download('''omw-1.4'''   )







		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__=0.9,	UpperCamelCase__=3,	UpperCamelCase__=0.5   ):







								"""simple docstring"""



								if NLTK_VERSION >= version.Version('''3.6.5'''   ):
														lowerCAmelCase_      			=  [
														    meteor_score.single_meteor_score(
														        word_tokenize(a_   ),	word_tokenize(a_   ),	alpha=a_,	beta=a_,	gamma=a_   )
														    for ref, pred in zip(a_,	a_   )
														]
								else:
														lowerCAmelCase_      			=  [
														    meteor_score.single_meteor_score(a_,	a_,	alpha=a_,	beta=a_,	gamma=a_   )
														    for ref, pred in zip(a_,	a_   )
														]

								return {"meteor": np.mean(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						(      *_UpperCAmelCase							:					List[str]						):







		'''simple docstring'''





		with open(_UpperCAmelCase ,   '''r'''						) as fh:
				fcntl.flock(_UpperCAmelCase ,   fcntl.LOCK_EX						)
				try:
						print(*_UpperCAmelCase						)
				finally:
						fcntl.flock(_UpperCAmelCase ,   fcntl.LOCK_UN						)


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

__A   			=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
				__A   			=dist.get_rank()
				__A   			=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





