





# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json

class SCREAMING_SNAKE_CASE_			(       lowercase_ ):





					"""simple docstring"""


					@classmethod
					def        __magic_name__			(			cls						):
										return cls






					@slow
					@require_torch_gpu
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.get_auto_remove_tmp_dir()
										lowerCamelCase__      =							F"\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        ".split()

										# we want this test to run even if there is only one GPU, but if there are more we use them all
										lowerCamelCase__      =							get_gpu_count()
										lowerCamelCase__      =							F"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
										lowerCamelCase__      =							[sys.executable] + distributed_args + args
										execute_subprocess_async(_lowerCAmelCase						,	env=self.get_env()						)

										lowerCamelCase__      =							os.path.join(_lowerCAmelCase						,	"test_bleu.json"						)
										lowerCamelCase__      =							load_json(_lowerCAmelCase						)
										# print(metrics)
										self.assertGreaterEqual(metrics["bleu"]						,	25						)








# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    "--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/

