

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
lowerCAmelCase__						=	{
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any] )     -> Union[str, Any]:
				A__            =     search.split()
				A__            =     dict((g.split("=" ) for g in groups) )
				A__            =     list(entries.keys() )
				A__            =     [[F'--{k} {v}' for v in vs.split(":" )] for k, vs in entries.items()]
				A__            =     [list(_A ) for x in itertools.product(*_A )]
				return matrix, entry_names



def    lowerCAmelCase__							(		)     -> List[Any]:
				A__            =     sys.argv[0]

				A__            =     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."
				    )		,    )
				A__      ,			A__            =     parser.parse_known_args()
				# we share some of the args
				args_main.extend(["--task", args.task] )
				A__            =     [prog] + args_main

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

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

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

								A__            =     run_generate(verbose=_A )
								# make sure scores are first in the table
								A__            =     OrderedDict()
								for score in task_score_names[task]:
												A__            =     scores[score]
								result.update(_A )
								results.append(_A )

								# find widest entries
								for k, v in result.items():
												A__            =     len(str(_A ) )
												if l > col_widths[k]:
																A__            =     l

				A__            =     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] ) )

				A__            =     results_sorted[0]
				for score in task_score_names[task]:
								del best[score]
				A__            =     [F'--{k} {v}' for k, v in best.items()]
				A__            =     ["--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()




import math



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int )     -> bool:




				'''simple docstring'''





				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5		,    int(math.sqrt(SCREAMING_SNAKE_CASE_ ) + 1 )		,    6 ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int = 2_0_0_0_0_0_0 )     -> int:




				'''simple docstring'''





				return sum(num for num in range(3		,    SCREAMING_SNAKE_CASE_		,    2 ) if is_prime(SCREAMING_SNAKE_CASE_ ) ) + 2 if n > 2 else 0


if __name__ == "__main__":
	print(f"""{solution() = }""")

