




'''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
lowercase						:   Optional[Any]      						=							{
    '''translation''': ['''bleu'''],
    '''summarization''': ROUGE_KEYS,
}




def 		a_(			lowerCamelCase   )					->Optional[int]:






       '''simple docstring'''




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




def 		a_(			)					->Optional[int]:






       '''simple docstring'''




       lowercase	:		Union[str, 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=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.'''
           ) ,					)
       lowercase,  lowercase	:		int    =   parser.parse_known_args()
       # we share some of the args
       args_main.extend(['''--task''', args.task]   )
       lowercase	:		Optional[Any]    =   [prog] + args_main

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

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

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

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

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

       lowercase	:		Tuple    =   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]   )   )

       lowercase	:		List[Any]    =   results_sorted[0]
       for score in task_score_names[task]:
              del best[score]
       lowercase	:		Optional[int]    =   [f"""--{k} {v}""" for k, v in best.items()]
       lowercase	:		List[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'''






def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->int:






       '''simple docstring'''




       if len(lowerCamelCase   ) < len(lowerCamelCase   ):
              return levenshtein_distance(lowerCamelCase ,					lowerCamelCase   )

       if len(lowerCamelCase   ) == 0:
              return len(lowerCamelCase   )

       lowercase	:		List[Any]    =   list(range(len(lowerCamelCase   ) + 1   )   )

       for i, ca in enumerate(lowerCamelCase   ):
              lowercase	:		Any    =   [i + 1]

              for j, ca in enumerate(lowerCamelCase   ):
                     # Calculate insertions, deletions and substitutions
                     lowercase	:		List[Any]    =   previous_row[j + 1] + 1
                     lowercase	:		str    =   current_row[j] + 1
                     lowercase	:		List[Any]    =   previous_row[j] + (ca != ca)

                     # Get the minimum to append to the current row
                     current_row.append(min(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )   )

              # Store the previous row
              lowercase	:		List[str]    =   current_row

       # Returns the last element (distance)
       return previous_row[-1]


if __name__ == "__main__":
      lowercase						:   Dict      						=							input('''Enter the first word:\n''').strip()
      lowercase						:   Tuple      						=							input('''Enter the second word:\n''').strip()

      lowercase						:   Tuple      						=							levenshtein_distance(first_word, second_word)
      print(f'Levenshtein distance between {first_word} and {second_word} is {result}')




