






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





def 							lowercase__		(				):
				_SCREAMING_SNAKE_CASE      :		str							=						sys.argv[0]

				_SCREAMING_SNAKE_CASE      :		str							=						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=lowerCamelCase,	required=lowerCamelCase,	help='param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"',	)
				parser.add_argument(
				    '--bs',	type=lowerCamelCase,	default=8,	required=lowerCamelCase,	help='initial batch size (may get reduced if it\'s too big)'					)
				parser.add_argument('--task',	type=lowerCamelCase,	help='used for task_specific_params + metrics'					)
				parser.add_argument(
				    '--info',	nargs='?',	type=lowerCamelCase,	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.'
				    ),	)
				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		List[str]							=						parser.parse_known_args()
				# we share some of the args
				args_main.extend(['--task', args.task]					)
				_SCREAMING_SNAKE_CASE      :		Any							=						[prog] + args_main

				# to support variations like translation_en_to_de"
				_SCREAMING_SNAKE_CASE      :		Any							=						'translation' if 'translation' in args.task else 'summarization'

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

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

								_SCREAMING_SNAKE_CASE      :		List[Any]							=						run_generate(verbose=lowerCamelCase					)
								# make sure scores are first in the table
								_SCREAMING_SNAKE_CASE      :		Dict							=						OrderedDict()
								for score in task_score_names[task]:
												_SCREAMING_SNAKE_CASE      :		Any							=						scores[score]
								result.update(lowerCamelCase					)
								results.append(lowerCamelCase					)

								# find widest entries
								for k, v in result.items():
												_SCREAMING_SNAKE_CASE      :		Optional[int]							=						len(str(lowerCamelCase					)					)
												if l > col_widths[k]:
																_SCREAMING_SNAKE_CASE      :		Tuple							=						l

				_SCREAMING_SNAKE_CASE      :		str							=						sorted(lowerCamelCase,	key=operator.itemgetter(*task_score_names[task]					),	reverse=lowerCamelCase					)
				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]					)					)

				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						results_sorted[0]
				for score in task_score_names[task]:
								del best[score]
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						[f"""--{k} {v}""" for k, v in best.items()]
				_SCREAMING_SNAKE_CASE      :		str							=						['--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"""
from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims

