




'''simple docstring'''





def 				UpperCamelCase	(  snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= len(snake_case   )
   SCREAMING_SNAKE_CASE__			:				List[Any]       						= [0] * length
   SCREAMING_SNAKE_CASE__			:				Any       						= [[False for i in range(snake_case   )] for j in range(snake_case   )]
   for i, c in enumerate(snake_case   ):
      SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= i
      for j in range(i + 1   ):
         if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
            SCREAMING_SNAKE_CASE__			:				str       						= True
            SCREAMING_SNAKE_CASE__			:				List[str]       						= min(snake_case	,     0 if j == 0 else (cut[j - 1] + 1)   )
      SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= mincut
   return cut[length - 1]


if __name__ == "__main__":
       lowercase__			=	input('''Enter the string: ''').strip()
       lowercase__			=	find_minimum_partitions(s)
       print(F"Minimum number of partitions required for the '{s}' is {ans}")







'''simple docstring'''





import argparse
import json
import re
import string
import sys
from collections import Counter


def 				UpperCamelCase	(  snake_case   ):




   '''simple docstring'''

   def remove_articles(snake_case   ):
      return re.sub(R'''\b(a|an|the)\b'''	,     ''' '''	,     snake_case   )

   def white_space_fix(snake_case   ):
      return " ".join(text.split()   )

   def remove_punc(snake_case   ):
      SCREAMING_SNAKE_CASE__			:				Optional[int]       						= set(string.punctuation   )
      return "".join(ch for ch in text if ch not in exclude   )

   def lower(snake_case   ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(snake_case   )   )   )   )


def 				UpperCamelCase	(  snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Tuple       						= normalize_answer(snake_case   ).split()
   SCREAMING_SNAKE_CASE__			:				List[Any]       						= normalize_answer(snake_case   ).split()
   SCREAMING_SNAKE_CASE__			:				Dict       						= Counter(snake_case   ) & Counter(snake_case   )
   SCREAMING_SNAKE_CASE__			:				int       						= sum(common.values()   )
   if num_same == 0:
      return 0
   SCREAMING_SNAKE_CASE__			:				Tuple       						= 1.0 * num_same / len(snake_case   )
   SCREAMING_SNAKE_CASE__			:				Tuple       						= 1.0 * num_same / len(snake_case   )
   SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= (2 * precision * recall) / (precision + recall)
   return fa


def 				UpperCamelCase	(  snake_case	,     snake_case   ):




   '''simple docstring'''

   return normalize_answer(snake_case   ) == normalize_answer(snake_case   )


def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Any       						= []
   for ground_truth in ground_truths:
      SCREAMING_SNAKE_CASE__			:				List[Any]       						= metric_fn(snake_case	,     snake_case   )
      scores_for_ground_truths.append(snake_case   )
   return max(snake_case   )


def 				UpperCamelCase	(  snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				str       						= 0
   SCREAMING_SNAKE_CASE__			:				Optional[int]       						= []
   for passage in dataset:
      for qa in passage["qas"]:
         total += 1
         if qa["id"] not in predictions:
            SCREAMING_SNAKE_CASE__			:				Any       						= f"""Unanswered question {qa["id"]} will receive score 0."""
            print(snake_case	,     file=sys.stderr   )
            continue

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= [x['''text'''] for x in qa['''answers''']]
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= predictions[qa['''id''']]

         SCREAMING_SNAKE_CASE__			:				List[str]       						= metric_max_over_ground_truths(snake_case	,     snake_case	,     snake_case   )
         if int(_exact_match   ) == 1:
            correct_ids.append(qa['''id''']   )
         exact_match += _exact_match

         fa += metric_max_over_ground_truths(snake_case	,     snake_case	,     snake_case   )

   SCREAMING_SNAKE_CASE__			:				int       						= exact_match / total
   SCREAMING_SNAKE_CASE__			:				Optional[int]       						= fa / total

   return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
       lowercase__			=	'''1.0'''
       lowercase__			=	argparse.ArgumentParser('''Official evaluation script for ReCoRD v1.0.''')
       parser.add_argument('''data_file''', help='''The dataset file in JSON format.''')
       parser.add_argument('''pred_file''', help='''The model prediction file in JSON format.''')
       parser.add_argument('''--output_correct_ids''', action='''store_true''', help='''Output the correctly answered query IDs.''')
       lowercase__			=	parser.parse_args()

       with open(args.data_file) as data_file:
              lowercase__			=	json.load(data_file)
              if dataset_json["version"] != expected_version:
                     print(
                         F"Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}",
                         file=sys.stderr,
                     )
              lowercase__			=	dataset_json['''data''']

       with open(args.pred_file) as pred_file:
              lowercase__			=	json.load(pred_file)

       lowercase__	,     lowercase__			=	evaluate(dataset, predictions)

       if args.output_correct_ids:
              print(F"Output {len(correct_ids)} correctly answered question IDs.")
              with open('''correct_ids.json''', '''w''') as f:
                     json.dump(correct_ids, f)

