




import io
import json
import subprocess


SCREAMING_SNAKE_CASE__	:				Optional[int]  =       [
    ['en', 'ru'],
    ['ru', 'en'],
    ['en', 'de'],
    ['de', 'en'],
]

SCREAMING_SNAKE_CASE__	:				Dict  =       8

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				Dict      			= {}
       for src, tgt in pairs:
              lowerCamelCase       :				List[str]      			= f'''{src}-{tgt}'''
              lowerCamelCase       :				int      			= f'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
              lowerCamelCase       :				List[str]      			= subprocess.run(_SCREAMING_SNAKE_CASE			,stdout=subprocess.PIPE   ).stdout.decode("utf-8"   ).splitlines()
              lowerCamelCase       :				Any      			= f'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
              lowerCamelCase       :				Optional[Any]      			= subprocess.run(_SCREAMING_SNAKE_CASE			,stdout=subprocess.PIPE   ).stdout.decode("utf-8"   ).splitlines()
              lowerCamelCase       :				Union[str, Any]      			= {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
       return text


SCREAMING_SNAKE_CASE__	:				Dict  =       get_all_data(pairs, n_objs)
SCREAMING_SNAKE_CASE__	:				Optional[int]  =       './fsmt_val_data.json'
with io.open(filename, 'w', encoding='utf-8') as f:
       SCREAMING_SNAKE_CASE__	:				Tuple  =       json.dump(text, f, indent=2, ensure_ascii=False)












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'])






