
"""simple docstring"""




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment



def 						__magic_name__		(				*UpperCamelCase    :		List[Any]    )    ->	str:
    a__    = 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    ):
        a__    = 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
    a		:					int    	=					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
    a		:					Tuple    	=					[
        'pip:./',
        'transformers',
        'datasets',
        'evaluate',
        'tqdm',
        'scipy',
        'scikit-learn',
        'tensorboard',
        'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
    ]
    a		:					Optional[Any]    	=					rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

    # Define train args/config, run train function
    a		:					Optional[Any]    	=					argparse.Namespace(cpu=False, mixed_precision='fp16')
    a		:					Tuple    	=					{'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'])




"""simple docstring"""




import shutil

import requests



def 						__magic_name__		(				UpperCamelCase    :		str       ,		UpperCamelCase    :		bool = False       ,		UpperCamelCase    :		str = "."    )    ->	dict:
    a__    = 'https://api.nasa.gov/planetary/apod'
    return requests.get(UpperCamelCase       ,		params={'api_key': api_key}    ).json()



def 						__magic_name__		(				UpperCamelCase    :		str       ,		UpperCamelCase    :		str = "."    )    ->	dict:
    a__    = get_apod_data(UpperCamelCase    )
    a__    = apod_data['url']
    a__    = img_url.split('/'    )[-1]
    a__    = requests.get(UpperCamelCase       ,		stream=UpperCamelCase    )

    with open(f'{path}/{img_name}'       ,		'wb+'    ) as img_file:
        shutil.copyfileobj(response.raw       ,		UpperCamelCase    )
    del response
    return apod_data



def 						__magic_name__		(				UpperCamelCase    :		str    )    ->	dict:
    a__    = 'https://images-api.nasa.gov/search'
    return requests.get(UpperCamelCase       ,		params={'q': query}    ).json()


if __name__ == "__main__":
    print(save_apod('YOUR API KEY'))
    a		:					List[Any]    	=					get_archive_data('apollo 2011')['collection']['items']
    print(apollo_aaaa_items[0]['data'][0]['description'])


