






"""simple docstring"""







import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
a :				int      					=		{
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}





def 						lowercase__(A						)				->List[Any]:




						"""simple docstring"""



						lowercase__				:      Any=       search.split()
						lowercase__				:      Union[str, Any]=       dict((g.split("="						) for g in groups)						)
						lowercase__				:      List[Any]=       list(entries.keys()						)
						lowercase__				:      str=       [[f'''--{k} {v}''' for v in vs.split(":"						)] for k, vs in entries.items()]
						lowercase__				:      Optional[Any]=       [list(A						) for x in itertools.product(*A						)]
						return matrix, entry_names





def 						lowercase__()				->Optional[int]:




						"""simple docstring"""



						lowercase__				:      Optional[Any]=       sys.argv[0]

						lowercase__				:      Optional[int]=       argparse.ArgumentParser(
						    usage=(
						        "\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore"
						        " refer to `run_eval.py -h` for the complete list."
						    )						)
						parser.add_argument(
						    "--search"					,		type=A					,		required=A					,		help="param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\""					,		)
						parser.add_argument(
						    "--bs"					,		type=A					,		default=8					,		required=A					,		help="initial batch size (may get reduced if it's too big)"						)
						parser.add_argument("--task"					,		type=A					,		help="used for task_specific_params + metrics"						)
						parser.add_argument(
						    "--info"					,		nargs="?"					,		type=A					,		const=datetime_now()					,		help=(
						        "add custom notes to be printed before the results table. If no value is passed, the current datetime"
						        " string will be used."
						    )					,		)
						lowercase__,	lowercase__				:      List[str]=       parser.parse_known_args()
						# we share some of the args
						args_main.extend(["--task", args.task]						)
						lowercase__				:      str=       [prog] + args_main

						# to support variations like translation_en_to_de"
						lowercase__				:      int=       "translation" if "translation" in args.task else "summarization"

						lowercase__,	lowercase__				:      Any=       parse_search_arg(args.search						)
						lowercase__				:      Tuple=       task_score_names[task]  # score cols first
						lowercase__				:      Any=       {col: len(str(A						)						) for col in col_names}
						lowercase__				:      List[str]=       []
						for r in matrix:
												lowercase__				:      Union[str, Any]=       dict((x.replace("--"					,		""						).split() for x in r)						)
												lowercase__				:      Tuple=       " ".join(A						).split()
												args_exp.extend(["--bs", str(args.bs						)]						)  # in case we need to reduce its size due to CUDA OOM
												lowercase__				:      Tuple=       args_normal + args_exp

												# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

												lowercase__				:      Any=       run_generate(verbose=A						)
												# make sure scores are first in the table
												lowercase__				:      Union[str, Any]=       OrderedDict()
												for score in task_score_names[task]:
																		lowercase__				:      int=       scores[score]
												result.update(A						)
												results.append(A						)

												# find widest entries
												for k, v in result.items():
																		lowercase__				:      List[Any]=       len(str(A						)						)
																		if l > col_widths[k]:
																								lowercase__				:      Optional[Any]=       l

						lowercase__				:      Tuple=       sorted(A					,		key=operator.itemgetter(*task_score_names[task]						)					,		reverse=A						)
						print(" | ".join([f'''{col:{col_widths[col]}}''' for col in col_names]						)						)
						print(" | ".join([f'''{'-'*col_widths[col]}''' for col in col_names]						)						)
						for row in results_sorted:
												print(" | ".join([f'''{row[col]:{col_widths[col]}}''' for col in col_names]						)						)

						lowercase__				:      List[str]=       results_sorted[0]
						for score in task_score_names[task]:
												del best[score]
						lowercase__				:      Optional[Any]=       [f'''--{k} {v}''' for k, v in best.items()]
						lowercase__				:      Union[str, Any]=       ["--bs", str(args.bs						)]
						if args.info:
												print(f'''\nInfo: {args.info}'''						)
						print("\nBest score args:"						)
						print(" ".join(args_main + best_args + dyn_args						)						)

						return results_sorted


if __name__ == "__main__":
			# Usage:
			# [normal-run_eval_search.py cmd plus] \
			# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
			#
			# Example:
			# PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
			# $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
			# --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
			# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
			run_search()













'''simple docstring'''







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

import torch_xla.distributed.xla_multiprocessing as xmp





def    _lowerCAmelCase						(						)					->					List[str]:
			__lowerCAmelCase											=				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    _lowerCAmelCase						(						)					->					Any:
			__lowerCAmelCase											=				parse_args()

			# Import training_script as a module.
			__lowerCAmelCase											=				Path(args.training_script	)
			sys.path.append(str(script_fpath.parent.resolve()	)	)
			__lowerCAmelCase											=				script_fpath.stem
			__lowerCAmelCase											=				importlib.import_module(lowercase	)

			# Patch sys.argv
			__lowerCAmelCase											=				[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()

