


'''simple docstring'''






def A_					(				__SCREAMING_SNAKE_CASE :					str       )     ->  int:
					__SCREAMING_SNAKE_CASE					:							Tuple			   = len(__SCREAMING_SNAKE_CASE       )
					__SCREAMING_SNAKE_CASE					:							Dict			   = [0] * length
					__SCREAMING_SNAKE_CASE					:							str			   = [[False for i in range(__SCREAMING_SNAKE_CASE       )] for j in range(__SCREAMING_SNAKE_CASE       )]
					for i, c in enumerate(__SCREAMING_SNAKE_CASE       ):
										__SCREAMING_SNAKE_CASE					:							Tuple			   = 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					:							Optional[int]			   = True
																				__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = min(__SCREAMING_SNAKE_CASE	,   0 if j == 0 else (cut[j - 1] + 1)       )
										__SCREAMING_SNAKE_CASE					:							Any			   = mincut
					return cut[length - 1]


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






'''simple docstring'''






# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    """--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"""
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/


