
'''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
UpperCamelCase__ :						Union[str, Any]    							=					{
    "translation": ["bleu"],
    "summarization": ROUGE_KEYS,
}
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			search.split()
   _SCREAMING_SNAKE_CASE       	=			dict((g.split("""="""  ) for g in groups)  )
   _SCREAMING_SNAKE_CASE       	=			list(entries.keys()  )
   _SCREAMING_SNAKE_CASE       	=			[[F"--{k} {v}" for v in vs.split(""":"""  )] for k, vs in entries.items()]
   _SCREAMING_SNAKE_CASE       	=			[list(SCREAMING_SNAKE_CASE_  ) for x in itertools.product(*SCREAMING_SNAKE_CASE_  )]
   return matrix, entry_names
def 				lowerCAmelCase_		(			)       ->       List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			sys.argv[0]

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

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

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

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

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

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

   _SCREAMING_SNAKE_CASE       	=			sorted(SCREAMING_SNAKE_CASE_      , key=operator.itemgetter(*task_score_names[task]  )      , reverse=SCREAMING_SNAKE_CASE_  )
   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       	=			results_sorted[0]
   for score in task_score_names[task]:
      del best[score]
   _SCREAMING_SNAKE_CASE       	=			[F"--{k} {v}" for k, v in best.items()]
   _SCREAMING_SNAKE_CASE       	=			["""--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 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       str:




   """simple docstring"""
   return " ".join(sorted(set(sentence.split()  )  )  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()






