





import requests

UpperCAmelCase_							=				"""YOUR API KEY"""





def  __magic_name__  (				lowercase				,       lowercase = giphy_api_key				)				-> list:

							"""simple docstring"""



							lowercase_	:   Dict  				=     """+""".join(query.split()				)
							lowercase_	:   int  				=     f"""https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"""
							lowercase_	:   Optional[int]  				=     requests.get(lowercase				).json()["""data"""]
							return [gif["url"] for gif in gifs]


if __name__ == "__main__":
							print("""\n""".join(get_gifs("""space ship""")))






import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def  __magic_name__  (				)				-> str:

							"""simple docstring"""



							lowercase_	:   Optional[int]  				=     ArgumentParser(
							    description=(
							        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
							    )				)

							# Optional arguments for the launch helper
							parser.add_argument("""--num_cores"""				,       type=lowercase				,       default=1				,       help="""Number of TPU cores to use (1 or 8)."""				)

							# positional
							parser.add_argument(
							    """training_script"""				,       type=lowercase				,       help=(
							        """The full path to the single TPU training """
							        """program/script to be launched in parallel, """
							        """followed by all the arguments for the """
							        """training script"""
							    )				,       )

							# rest from the training program
							parser.add_argument("""training_script_args"""				,       nargs=lowercase				)

							return parser.parse_args()





def  __magic_name__  (				)				-> List[Any]:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     parse_args()

							# Import training_script as a module.
							lowercase_	:   List[Any]  				=     Path(args.training_script				)
							sys.path.append(str(script_fpath.parent.resolve()				)				)
							lowercase_	:   Tuple  				=     script_fpath.stem
							lowercase_	:   List[str]  				=     importlib.import_module(lowercase				)

							# Patch sys.argv
							lowercase_	:   Optional[int]  				=     [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores				)]

							xmp.spawn(mod._mp_fn				,       args=()				,       nprocs=args.num_cores				)


if __name__ == "__main__":
							main()