

"""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	:       List[str]        =						{
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					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(lowercase__ ) for x in itertools.product(*lowercase__ )]
					return matrix, entry_names
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					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=lowercase__     ,				required=lowercase__     ,				help="param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\""     ,				)
					parser.add_argument(
					    "--bs"     ,				type=lowercase__     ,				default=8     ,				required=lowercase__     ,				help="initial batch size (may get reduced if it's too big)" )
					parser.add_argument("--task"     ,				type=lowercase__     ,				help="used for task_specific_params + metrics" )
					parser.add_argument(
					    "--info"     ,				nargs="?"     ,				type=lowercase__     ,				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(lowercase__ ) ) for col in col_names}
					A     =   []
					for r in matrix:
										A     =   dict((x.replace("--"     ,				"" ).split() for x in r) )
										A     =   " ".join(lowercase__ ).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=lowercase__ )
										# make sure scores are first in the table
										A     =   OrderedDict()
										for score in task_score_names[task]:
															A     =   scores[score]
										result.update(lowercase__ )
										results.append(lowercase__ )

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

					A     =   sorted(lowercase__     ,				key=operator.itemgetter(*task_score_names[task] )     ,				reverse=lowercase__ )
					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()








"""simple docstring"""




def   __SCREAMING_SNAKE_CASE		(		lowercase__ = 30 ):
					"""simple docstring"""
					A     =   exponent + 2
					A     =   (1 + 5**0.5) / 2
					A     =   (phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

					return int(lowercase__ )


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





