




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def 					A				(				*_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				List[str]      			= torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''   )
       with patch_environment(
           world_size=_SCREAMING_SNAKE_CASE			,master_addr="127.0.01"			,master_port="29500"			,mixed_precision=args[1].mixed_precision   ):
              lowerCamelCase       :				Union[str, Any]      			= PrepareForLaunch(_SCREAMING_SNAKE_CASE			,distributed_type="MULTI_GPU"   )
              torch.multiprocessing.start_processes(_SCREAMING_SNAKE_CASE			,args=_SCREAMING_SNAKE_CASE			,nprocs=_SCREAMING_SNAKE_CASE			,start_method="spawn"   )


if __name__ == "__main__":
       # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
       # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

       # on-demand GPU
       # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
       SCREAMING_SNAKE_CASE__	:				Tuple  =       rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
       gpu.up_if_not()

       # on-prem GPU
       # gpu = rh.cluster(
       #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
       #       )

       # Set up remote function
       SCREAMING_SNAKE_CASE__	:				int  =       [
           'pip:./',
           'transformers',
           'datasets',
           'evaluate',
           'tqdm',
           'scipy',
           'scikit-learn',
           'tensorboard',
           'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
       ]
       SCREAMING_SNAKE_CASE__	:				Dict  =       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

       # Define train args/config, run train function
       SCREAMING_SNAKE_CASE__	:				int  =       argparse.Namespace(cpu=False, mixed_precision='fp16')
       SCREAMING_SNAKE_CASE__	:				int  =       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       launch_train_gpu(config, train_args, stream_logs=True)

       # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
       # gpu.install_packages(reqs)
       # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])












# 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/






