




import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch

def 					A				(				)     ->			Dict:
       lowerCamelCase       :				Any      			= Dataset.from_dict({"i": range(17   )}   )
       lowerCamelCase       :				List[Any]      			= len(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[str]      			= 3
       lowerCamelCase       :				List[Any]      			= [
           split_dataset_by_node(_SCREAMING_SNAKE_CASE			,rank=_SCREAMING_SNAKE_CASE			,world_size=_SCREAMING_SNAKE_CASE   ) for rank in range(_SCREAMING_SNAKE_CASE   )
       ]
       assert sum(len(_SCREAMING_SNAKE_CASE   ) for ds in datasets_per_rank   ) == full_size
       assert len({tuple(x.values()   ) for ds in datasets_per_rank for x in ds}   ) == full_size

def 					A				(				)     ->			Optional[Any]:
       def gen():
              return ({"i": i} for i in range(17   ))

       lowerCamelCase       :				Any      			= 3
       lowerCamelCase       :				Optional[int]      			= IterableDataset.from_generator(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Optional[int]      			= len(list(_SCREAMING_SNAKE_CASE   )   )
       lowerCamelCase       :				List[str]      			= [
           split_dataset_by_node(_SCREAMING_SNAKE_CASE			,rank=_SCREAMING_SNAKE_CASE			,world_size=_SCREAMING_SNAKE_CASE   ) for rank in range(_SCREAMING_SNAKE_CASE   )
       ]
       assert sum(len(list(_SCREAMING_SNAKE_CASE   )   ) for ds in datasets_per_rank   ) == full_size
       assert len({tuple(x.values()   ) for ds in datasets_per_rank for x in ds}   ) == full_size

@pytest.mark.parametrize("shards_per_node"			,[1, 2, 3]   )
def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			Optional[Any]:
       def gen(_SCREAMING_SNAKE_CASE   ):
              for shard in shards:
                     yield from ({"i": i, "shard": shard} for i in range(17   ))

       lowerCamelCase       :				Optional[int]      			= 3
       lowerCamelCase       :				Dict      			= shards_per_node * world_size
       lowerCamelCase       :				Dict      			= {"shards": [f'''shard_{shard_idx}.txt''' for shard_idx in range(_SCREAMING_SNAKE_CASE   )]}
       lowerCamelCase       :				Dict      			= IterableDataset.from_generator(_SCREAMING_SNAKE_CASE			,gen_kwargs=_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Optional[Any]      			= len(list(_SCREAMING_SNAKE_CASE   )   )
       assert full_ds.n_shards == world_size * shards_per_node
       lowerCamelCase       :				Optional[int]      			= [
           split_dataset_by_node(_SCREAMING_SNAKE_CASE			,rank=_SCREAMING_SNAKE_CASE			,world_size=_SCREAMING_SNAKE_CASE   ) for rank in range(_SCREAMING_SNAKE_CASE   )
       ]
       assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
       assert sum(len(list(_SCREAMING_SNAKE_CASE   )   ) for ds in datasets_per_rank   ) == full_size
       assert len({tuple(x.values()   ) for ds in datasets_per_rank for x in ds}   ) == full_size

def 					A				(				)     ->			List[str]:
       def gen():
              return ({"i": i} for i in range(17   ))

       lowerCamelCase       :				Union[str, Any]      			= 2
       lowerCamelCase       :				Optional[int]      			= IterableDataset.from_generator(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Union[str, Any]      			= len(list(_SCREAMING_SNAKE_CASE   )   )

       lowerCamelCase       :				Optional[int]      			= split_dataset_by_node(_SCREAMING_SNAKE_CASE			,rank=0			,world_size=_SCREAMING_SNAKE_CASE   ).shuffle(seed=42   )
       assert len(list(_SCREAMING_SNAKE_CASE   )   ) == 1 + full_size // world_size
       with pytest.raises(_SCREAMING_SNAKE_CASE   ):
              split_dataset_by_node(_SCREAMING_SNAKE_CASE			,rank=0			,world_size=_SCREAMING_SNAKE_CASE   ).shuffle()

       lowerCamelCase       :				Dict      			= split_dataset_by_node(full_ds.shuffle(seed=42   )			,rank=0			,world_size=_SCREAMING_SNAKE_CASE   )
       assert len(list(_SCREAMING_SNAKE_CASE   )   ) == 1 + full_size // world_size
       with pytest.raises(_SCREAMING_SNAKE_CASE   ):
              split_dataset_by_node(full_ds.shuffle()			,rank=0			,world_size=_SCREAMING_SNAKE_CASE   )

@pytest.mark.parametrize("streaming"			,[False, True]   )
@require_torch
@pytest.mark.skipif(os.name == "nt"			,reason="execute_subprocess_async doesn't support windows"   )
@pytest.mark.integration
def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				str      			= 2
       lowerCamelCase       :				List[str]      			= get_torch_dist_unique_port()
       lowerCamelCase       :				Tuple      			= Path(_SCREAMING_SNAKE_CASE   ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
       lowerCamelCase       :				Optional[Any]      			= f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
       lowerCamelCase       :				List[str]      			= f'''
        --streaming={streaming}
    '''.split()
       lowerCamelCase       :				Any      			= [sys.executable] + distributed_args + args
       execute_subprocess_async(_SCREAMING_SNAKE_CASE			,env=os.environ.copy()   )


@pytest.mark.parametrize(
    "nproc_per_node, num_workers"			,[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]			,)
@require_torch
@pytest.mark.skipif(os.name == "nt"			,reason="execute_subprocess_async doesn't support windows"   )
@pytest.mark.integration
def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				str      			= True
       lowerCamelCase       :				Optional[Any]      			= get_torch_dist_unique_port()
       lowerCamelCase       :				List[str]      			= Path(_SCREAMING_SNAKE_CASE   ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
       lowerCamelCase       :				Optional[int]      			= f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
       lowerCamelCase       :				str      			= f'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
       lowerCamelCase       :				int      			= [sys.executable] + distributed_args + args
       execute_subprocess_async(_SCREAMING_SNAKE_CASE			,env=os.environ.copy()   )












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'])






