



'''simple docstring'''
import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


snake_case_				:			List[Any]						 =						get_logger(__name__)


if TYPE_CHECKING:
					from .builder import DatasetBuilder






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_ = None							):
						_UpperCamelCase      :	Any				       =     importlib.import_module(UpperCAmelCase_							)

						# TODO(QL): always update the module to add subsequent new authentication
						if hasattr(UpperCAmelCase_						,						'_patched_for_streaming'							) and module._patched_for_streaming:
												return

						def wrap_auth(UpperCAmelCase_							):
												@wraps(UpperCAmelCase_							)
												def wrapper(*UpperCAmelCase_						,						**UpperCAmelCase_							):
																		return function(*UpperCAmelCase_						,						use_auth_token=UpperCAmelCase_						,						**UpperCAmelCase_							)

												_UpperCamelCase      :	Union[str, Any]				       =     'wrap_auth'
												return wrapper

						# open files in a streaming fashion
						patch_submodule(UpperCAmelCase_						,						'open'						,						wrap_auth(UpperCAmelCase_							)							).start()
						patch_submodule(UpperCAmelCase_						,						'os.listdir'						,						wrap_auth(UpperCAmelCase_							)							).start()
						patch_submodule(UpperCAmelCase_						,						'os.walk'						,						wrap_auth(UpperCAmelCase_							)							).start()
						patch_submodule(UpperCAmelCase_						,						'glob.glob'						,						wrap_auth(UpperCAmelCase_							)							).start()
						# allow to navigate in remote zip files
						patch_submodule(UpperCAmelCase_						,						'os.path.join'						,						UpperCAmelCase_							).start()
						patch_submodule(UpperCAmelCase_						,						'os.path.dirname'						,						UpperCAmelCase_							).start()
						patch_submodule(UpperCAmelCase_						,						'os.path.basename'						,						UpperCAmelCase_							).start()
						patch_submodule(UpperCAmelCase_						,						'os.path.relpath'						,						UpperCAmelCase_							).start()
						patch_submodule(UpperCAmelCase_						,						'os.path.split'						,						UpperCAmelCase_							).start()
						patch_submodule(UpperCAmelCase_						,						'os.path.splitext'						,						UpperCAmelCase_							).start()
						# allow checks on paths
						patch_submodule(UpperCAmelCase_						,						'os.path.exists'						,						wrap_auth(UpperCAmelCase_							)							).start()
						patch_submodule(UpperCAmelCase_						,						'os.path.isdir'						,						wrap_auth(UpperCAmelCase_							)							).start()
						patch_submodule(UpperCAmelCase_						,						'os.path.isfile'						,						wrap_auth(UpperCAmelCase_							)							).start()
						patch_submodule(UpperCAmelCase_						,						'os.path.getsize'						,						wrap_auth(UpperCAmelCase_							)							).start()
						patch_submodule(UpperCAmelCase_						,						'pathlib.Path'						,						UpperCAmelCase_							).start()
						# file readers
						patch_submodule(UpperCAmelCase_						,						'gzip.open'						,						wrap_auth(UpperCAmelCase_							)							).start()
						patch_submodule(UpperCAmelCase_						,						'numpy.load'						,						wrap_auth(UpperCAmelCase_							)							).start()
						patch_submodule(UpperCAmelCase_						,						'pandas.read_csv'						,						wrap_auth(UpperCAmelCase_							)						,						attrs=['__version__']							).start()
						patch_submodule(UpperCAmelCase_						,						'pandas.read_excel'						,						wrap_auth(UpperCAmelCase_							)						,						attrs=['__version__']							).start()
						patch_submodule(UpperCAmelCase_						,						'scipy.io.loadmat'						,						wrap_auth(UpperCAmelCase_							)						,						attrs=['__version__']							).start()
						patch_submodule(UpperCAmelCase_						,						'xml.etree.ElementTree.parse'						,						wrap_auth(UpperCAmelCase_							)							).start()
						patch_submodule(UpperCAmelCase_						,						'xml.dom.minidom.parse'						,						wrap_auth(UpperCAmelCase_							)							).start()
						_UpperCamelCase      :	int				       =     True






def A__					(  UpperCAmelCase_							):
						extend_module_for_streaming(builder.__module__						,						use_auth_token=builder.use_auth_token							)
						# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
						if not builder.__module__.startswith('datasets.'							):  # check that it's not a packaged builder like csv
												for imports in get_imports(inspect.getfile(builder.__class__							)							):
																		if imports[0] == "internal":
																								_UpperCamelCase      :	Any				       =     imports[1]
																								_UpperCamelCase      :	Union[str, Any]				       =     '.'.join(builder.__module__.split('.'							)[:-1] + [internal_import_name]							)
																								extend_module_for_streaming(UpperCAmelCase_						,						use_auth_token=builder.use_auth_token							)

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
						from .builder import DatasetBuilder

						_UpperCamelCase      :	Any				       =     [
						    cls.__module__
						    for cls in type(UpperCAmelCase_							).__mro__[1:]  # make sure it's not the same module we've already patched
						    if issubclass(cls						,						UpperCAmelCase_							) and cls.__module__ != DatasetBuilder.__module__
						]  # check it's not a standard builder from datasets.builder
						for module in parent_builder_modules:
												extend_module_for_streaming(UpperCAmelCase_						,						use_auth_token=builder.use_auth_token							)







'''simple docstring'''
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def A__					(  *UpperCAmelCase_							):
						_UpperCamelCase      :	str				       =     torch.cuda.device_count()
						print(f'Device count: {num_processes}'							)
						with patch_environment(
						    world_size=UpperCAmelCase_						,						master_addr='127.0.01'						,						master_port='29500'						,						mixed_precision=args[1].mixed_precision							):
												_UpperCamelCase      :	Optional[int]				       =     PrepareForLaunch(UpperCAmelCase_						,						distributed_type='MULTI_GPU'							)
												torch.multiprocessing.start_processes(UpperCAmelCase_						,						args=UpperCAmelCase_						,						nprocs=UpperCAmelCase_						,						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
					snake_case_				:			Optional[Any]						 =						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
					snake_case_				:			List[Any]						 =						[
					    'pip:./',
					    'transformers',
					    'datasets',
					    'evaluate',
					    'tqdm',
					    'scipy',
					    'scikit-learn',
					    'tensorboard',
					    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
					]
					snake_case_				:			Union[str, Any]						 =						rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

					# Define train args/config, run train function
					snake_case_				:			List[str]						 =						argparse.Namespace(cpu=False, mixed_precision='fp16')
					snake_case_				:			List[str]						 =						{'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'])


