


'''simple docstring'''






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCAmelCase    				=							logging.get_logger(__name__)

__UpperCAmelCase    				=							{
    """gpt2""": """https://huggingface.co/gpt2/resolve/main/config.json""",
    """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/config.json""",
    """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/config.json""",
    """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/config.json""",
    """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/config.json""",
}




class 		a__				(							__lowercase  ):





							'''simple docstring'''






							lowercase__			:   Union[str, Any]							=      '''gpt2'''
							lowercase__			:   Dict							=      ['''past_key_values''']
							lowercase__			:   List[str]							=      {
							    '''hidden_size''': '''n_embd''',
							    '''max_position_embeddings''': '''n_positions''',
							    '''num_attention_heads''': '''n_head''',
							    '''num_hidden_layers''': '''n_layer''',
							}







							def __init__(		self					,    lowerCamelCase_=5_02_57					,    lowerCamelCase_=10_24					,    lowerCamelCase_=7_68					,    lowerCamelCase_=12					,    lowerCamelCase_=12					,    lowerCamelCase_=None					,    lowerCamelCase_="gelu_new"					,    lowerCamelCase_=0.1					,    lowerCamelCase_=0.1					,    lowerCamelCase_=0.1					,    lowerCamelCase_=1e-5					,    lowerCamelCase_=0.02					,    lowerCamelCase_="cls_index"					,    lowerCamelCase_=True					,    lowerCamelCase_=None					,    lowerCamelCase_=True					,    lowerCamelCase_=0.1					,    lowerCamelCase_=True					,    lowerCamelCase_=True					,    lowerCamelCase_=5_02_56					,    lowerCamelCase_=5_02_56					,    lowerCamelCase_=False					,    lowerCamelCase_=False					,    **lowerCamelCase_					,    )       ->							int:
														lowerCAmelCase__          =	vocab_size
														lowerCAmelCase__          =	n_positions
														lowerCAmelCase__          =	n_embd
														lowerCAmelCase__          =	n_layer
														lowerCAmelCase__          =	n_head
														lowerCAmelCase__          =	n_inner
														lowerCAmelCase__          =	activation_function
														lowerCAmelCase__          =	resid_pdrop
														lowerCAmelCase__          =	embd_pdrop
														lowerCAmelCase__          =	attn_pdrop
														lowerCAmelCase__          =	layer_norm_epsilon
														lowerCAmelCase__          =	initializer_range
														lowerCAmelCase__          =	summary_type
														lowerCAmelCase__          =	summary_use_proj
														lowerCAmelCase__          =	summary_activation
														lowerCAmelCase__          =	summary_first_dropout
														lowerCAmelCase__          =	summary_proj_to_labels
														lowerCAmelCase__          =	scale_attn_weights
														lowerCAmelCase__          =	use_cache
														lowerCAmelCase__          =	scale_attn_by_inverse_layer_idx
														lowerCAmelCase__          =	reorder_and_upcast_attn

														lowerCAmelCase__          =	bos_token_id
														lowerCAmelCase__          =	eos_token_id

														super().__init__(bos_token_id=_UpperCamelCase					,    eos_token_id=_UpperCamelCase					,    **_UpperCamelCase   )




class 		a__				(							__lowercase  ):





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_ = "default"					,    lowerCamelCase_ = None					,    lowerCamelCase_ = False					,    )       ->							Optional[int]:
														super().__init__(_UpperCamelCase					,    task=_UpperCamelCase					,    patching_specs=_UpperCamelCase					,    use_past=_UpperCamelCase   )
														if not getattr(self._config					,    '''pad_token_id'''					,    _UpperCamelCase   ):
																					# TODO: how to do that better?
																					lowerCAmelCase__          =	0







							@property
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Dict:
														lowerCAmelCase__          =	OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}   )
														if self.use_past:
																					self.fill_with_past_key_values_(_UpperCamelCase					,    direction='''inputs'''   )
																					lowerCAmelCase__          =	{0: '''batch''', 1: '''past_sequence + sequence'''}
														else:
																					lowerCAmelCase__          =	{0: '''batch''', 1: '''sequence'''}

														return common_inputs







							@property
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[str]:
														return self._config.n_layer







							@property
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[int]:
														return self._config.n_head







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_ = -1					,    lowerCamelCase_ = -1					,    lowerCamelCase_ = False					,    lowerCamelCase_ = None					,    )       ->							List[str]:
														lowerCAmelCase__          =	super(_UpperCamelCase					,    self   ).generate_dummy_inputs(
														    _UpperCamelCase					,    batch_size=_UpperCamelCase					,    seq_length=_UpperCamelCase					,    is_pair=_UpperCamelCase					,    framework=_UpperCamelCase   )

														# We need to order the input in the way they appears in the forward()
														lowerCAmelCase__          =	OrderedDict({'''input_ids''': common_inputs['''input_ids''']}   )

														# Need to add the past_keys
														if self.use_past:
																					if not is_torch_available():
																												raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
																					else:
																												import torch

																												lowerCAmelCase__   ,					lowerCAmelCase__          =	common_inputs['''input_ids'''].shape
																												# Not using the same length for past_key_values
																												lowerCAmelCase__          =	seqlen + 2
																												lowerCAmelCase__          =	(
																												    batch,
																												    self.num_attention_heads,
																												    past_key_values_length,
																												    self._config.hidden_size // self.num_attention_heads,
																												)
																												lowerCAmelCase__          =	[
																												    (torch.zeros(_UpperCamelCase   ), torch.zeros(_UpperCamelCase   )) for _ in range(self.num_layers   )
																												]

														lowerCAmelCase__          =	common_inputs['''attention_mask''']
														if self.use_past:
																					lowerCAmelCase__          =	ordered_inputs['''attention_mask'''].dtype
																					lowerCAmelCase__          =	torch.cat(
																					    [ordered_inputs['''attention_mask'''], torch.ones(_UpperCamelCase					,    _UpperCamelCase					,    dtype=_UpperCamelCase   )]					,    dim=1   )

														return ordered_inputs







							@property
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[Any]:
														return 13







def 		_UpperCamelCase      (			UpperCamelCase_				:    str						)     ->      int:

						"""simple docstring"""



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


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





