


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A							     =       logging.get_logger(__name__)

_A							     =       {
    'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/config.json',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}

class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):
							_lowerCamelCase				: int       =					"""rembert"""





							def __init__(							self			,       _SCREAMING_SNAKE_CASE=25_0300			,       _SCREAMING_SNAKE_CASE=1152			,       _SCREAMING_SNAKE_CASE=32			,       _SCREAMING_SNAKE_CASE=18			,       _SCREAMING_SNAKE_CASE=256			,       _SCREAMING_SNAKE_CASE=1664			,       _SCREAMING_SNAKE_CASE=4608			,       _SCREAMING_SNAKE_CASE="gelu"			,       _SCREAMING_SNAKE_CASE=0.0			,       _SCREAMING_SNAKE_CASE=0.0			,       _SCREAMING_SNAKE_CASE=0.1			,       _SCREAMING_SNAKE_CASE=512			,       _SCREAMING_SNAKE_CASE=2			,       _SCREAMING_SNAKE_CASE=0.0_2			,       _SCREAMING_SNAKE_CASE=1E-12			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE=0			,       _SCREAMING_SNAKE_CASE=312			,       _SCREAMING_SNAKE_CASE=313			,       **_SCREAMING_SNAKE_CASE			,       ):
								super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE			,       bos_token_id=_SCREAMING_SNAKE_CASE			,       eos_token_id=_SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    )

								a_     			=   vocab_size
								a_     			=   input_embedding_size
								a_     			=   output_embedding_size
								a_     			=   max_position_embeddings
								a_     			=   hidden_size
								a_     			=   num_hidden_layers
								a_     			=   num_attention_heads
								a_     			=   intermediate_size
								a_     			=   hidden_act
								a_     			=   hidden_dropout_prob
								a_     			=   attention_probs_dropout_prob
								a_     			=   classifier_dropout_prob
								a_     			=   initializer_range
								a_     			=   type_vocab_size
								a_     			=   layer_norm_eps
								a_     			=   use_cache
								a_     			=   False







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							@property
							def   __magic_name__      (							self    ):
								if self.task == "multiple-choice":
									a_     			=   {0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
									a_     			=   {0: """batch""", 1: """sequence"""}
								return OrderedDict(
								    [
								        ("""input_ids""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								        ("""token_type_ids""", dynamic_axis),
								    ]    )





							@property
							def   __magic_name__      (							self    ):
								return 1E-4



def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	int:



	"""simple docstring"""






	a_     			=   len(UpperCamelCase	)
	a_     			=   [0] * length
	a_     			=   [[False for i in range(UpperCamelCase	)] for j in range(UpperCamelCase	)]
	for i, c in enumerate(UpperCamelCase	):
		a_     			=   i
		for j in range(i + 1	):
			if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
				a_     			=   True
				a_     			=   min(UpperCamelCase     ,					0 if j == 0 else (cut[j - 1] + 1)	)
		a_     			=   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}')