
"""simple docstring"""


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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCAmelCase			=		logging.get_logger(__name__)

lowerCAmelCase			=		{
    """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json""",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}

class 					A_			(  A__     ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     """m2m_100"""
							SCREAMING_SNAKE_CASE_			   =     ["""past_key_values"""]
							SCREAMING_SNAKE_CASE_			   =     {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





							def __init__(						self						:Union[str, Any]  ,							lowerCamelCase_						:List[str]=128_112  ,							lowerCamelCase_						:Optional[int]=1_024  ,							lowerCamelCase_						:Dict=12  ,							lowerCamelCase_						:List[Any]=4_096  ,							lowerCamelCase_						:Optional[int]=16  ,							lowerCamelCase_						:Optional[Any]=12  ,							lowerCamelCase_						:str=4_096  ,							lowerCamelCase_						:Tuple=16  ,							lowerCamelCase_						:Any=0.05  ,							lowerCamelCase_						:str=0.05  ,							lowerCamelCase_						:Tuple=True  ,							lowerCamelCase_						:Any=True  ,							lowerCamelCase_						:Dict="relu"  ,							lowerCamelCase_						:Union[str, Any]=1_024  ,							lowerCamelCase_						:Optional[int]=0.1  ,							lowerCamelCase_						:Union[str, Any]=0.1  ,							lowerCamelCase_						:str=0.0  ,							lowerCamelCase_						:str=0.02  ,							lowerCamelCase_						:Optional[int]=2  ,							lowerCamelCase_						:Union[str, Any]=True  ,							lowerCamelCase_						:List[Any]=1  ,							lowerCamelCase_						:Optional[Any]=0  ,							lowerCamelCase_						:Union[str, Any]=2  ,							**lowerCamelCase_						:Optional[Any]  ,							):





										"""simple docstring"""

										lowerCamelCase__			:  str  =vocab_size
										lowerCamelCase__			:  Tuple  =max_position_embeddings
										lowerCamelCase__			:  Any  =d_model
										lowerCamelCase__			:  Optional[Any]  =encoder_ffn_dim
										lowerCamelCase__			:  Dict  =encoder_layers
										lowerCamelCase__			:  List[str]  =encoder_attention_heads
										lowerCamelCase__			:  List[str]  =decoder_ffn_dim
										lowerCamelCase__			:  Optional[Any]  =decoder_layers
										lowerCamelCase__			:  Dict  =decoder_attention_heads
										lowerCamelCase__			:  Tuple  =dropout
										lowerCamelCase__			:  Union[str, Any]  =attention_dropout
										lowerCamelCase__			:  Union[str, Any]  =activation_dropout
										lowerCamelCase__			:  Dict  =activation_function
										lowerCamelCase__			:  int  =init_std
										lowerCamelCase__			:  Any  =encoder_layerdrop
										lowerCamelCase__			:  List[Any]  =decoder_layerdrop
										lowerCamelCase__			:  List[str]  =use_cache
										lowerCamelCase__			:  int  =encoder_layers
										lowerCamelCase__			:  Union[str, Any]  =scale_embedding  # scale factor will be sqrt(d_model) if True

										super().__init__(
										    pad_token_id=lowerCamelCase_  ,							bos_token_id=lowerCamelCase_  ,							eos_token_id=lowerCamelCase_  ,							is_encoder_decoder=lowerCamelCase_  ,							decoder_start_token_id=lowerCamelCase_  ,							**lowerCamelCase_  ,							)






class 					A_			(  A__     ):



							"""simple docstring"""






							@property
							def    UpperCAmelCase__							(						self						:List[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  int  =OrderedDict(
										    [
										        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
										        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
										    ] )

										if self.use_past:
													lowerCamelCase__			:  Dict  ={0: 'batch'}
													lowerCamelCase__			:  str  ={0: 'batch', 1: 'past_decoder_sequence + sequence'}
										else:
													lowerCamelCase__			:  Any  ={0: 'batch', 1: 'decoder_sequence'}
													lowerCamelCase__			:  int  ={0: 'batch', 1: 'decoder_sequence'}

										if self.use_past:
													self.fill_with_past_key_values_(lowerCamelCase_  ,							direction='inputs' )
										return common_inputs





							def    UpperCAmelCase__							(						self						:int  ,							lowerCamelCase_						:PreTrainedTokenizer  ,							lowerCamelCase_						:int = -1  ,							lowerCamelCase_						:int = -1  ,							lowerCamelCase_						:bool = False  ,							lowerCamelCase_						:Optional[TensorType] = None  ,							):





										"""simple docstring"""

										lowerCamelCase__			:  Any  =compute_effective_axis_dimension(
										    lowerCamelCase_  ,							fixed_dimension=OnnxConfig.default_fixed_batch  ,							num_token_to_add=0 )

										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										lowerCamelCase__			:  str  =tokenizer.num_special_tokens_to_add(lowerCamelCase_ )
										lowerCamelCase__			:  int  =compute_effective_axis_dimension(
										    lowerCamelCase_  ,							fixed_dimension=OnnxConfig.default_fixed_sequence  ,							num_token_to_add=lowerCamelCase_ )

										# Generate dummy inputs according to compute batch and sequence
										lowerCamelCase__			:  Tuple  =[' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
										lowerCamelCase__			:  Union[str, Any]  =dict(tokenizer(lowerCamelCase_  ,							return_tensors=lowerCamelCase_ ) )
										return common_inputs





							def    UpperCAmelCase__							(						self						:Optional[int]  ,							lowerCamelCase_						:PreTrainedTokenizer  ,							lowerCamelCase_						:int = -1  ,							lowerCamelCase_						:int = -1  ,							lowerCamelCase_						:bool = False  ,							lowerCamelCase_						:Optional[TensorType] = None  ,							):





										"""simple docstring"""

										lowerCamelCase__			:  str  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_ )

										# Generate decoder inputs
										lowerCamelCase__			:  Union[str, Any]  =seq_length if not self.use_past else 1
										lowerCamelCase__			:  int  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_ )
										lowerCamelCase__			:  str  ={f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
										lowerCamelCase__			:  int  =dict(**lowerCamelCase_  ,							**lowerCamelCase_ )

										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__			:  Optional[Any]  =common_inputs['input_ids'].shape
													lowerCamelCase__			:  Tuple  =common_inputs['decoder_input_ids'].shape[1]
													lowerCamelCase__ ,       lowerCamelCase__			:  Tuple  =self.num_attention_heads
													lowerCamelCase__			:  Dict  =(
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													lowerCamelCase__			:  Any  =decoder_seq_length + 3
													lowerCamelCase__			:  List[Any]  =(
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)

													lowerCamelCase__			:  str  =torch.cat(
													    [common_inputs['decoder_attention_mask'], torch.ones(lowerCamelCase_  ,							lowerCamelCase_ )]  ,							dim=1 )

													lowerCamelCase__			:  Tuple  =[]
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													lowerCamelCase__ ,       lowerCamelCase__			:  int  =self.num_layers
													lowerCamelCase__			:  Union[str, Any]  =min(lowerCamelCase_  ,							lowerCamelCase_ )
													lowerCamelCase__			:  str  =max(lowerCamelCase_  ,							lowerCamelCase_ ) - min_num_layers
													lowerCamelCase__			:  str  ='encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

													for _ in range(lowerCamelCase_ ):
																common_inputs["past_key_values"].append(
																    (
																        torch.zeros(lowerCamelCase_ ),
																        torch.zeros(lowerCamelCase_ ),
																        torch.zeros(lowerCamelCase_ ),
																        torch.zeros(lowerCamelCase_ ),
																    ) )
													# TODO: test this.
													lowerCamelCase__			:  Tuple  =encoder_shape if remaining_side_name == 'encoder' else decoder_shape
													for _ in range(lowerCamelCase_  ,							lowerCamelCase_ ):
																common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase_ ), torch.zeros(lowerCamelCase_ )) )
										return common_inputs


							SCREAMING_SNAKE_CASE_			   =     _generate_dummy_inputs_for_default_and_seqaseq_lm

"""simple docstring"""


def      lowerCAmelCase_     (	snake_case_	:    str    )     ->int:
			lowerCamelCase__			:  Optional[Any]  =len(snake_case_    )
			lowerCamelCase__			:  Any  =[0] * length
			lowerCamelCase__			:  str  =[[False for i in range(snake_case_    )] for j in range(snake_case_    )]
			for i, c in enumerate(snake_case_    ):
						lowerCamelCase__			:  Optional[Any]  =i
						for j in range(i + 1    ):
									if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
												lowerCamelCase__			:  int  =True
												lowerCamelCase__			:  Tuple  =min(snake_case_							,					0 if j == 0 else (cut[j - 1] + 1)    )
						lowerCamelCase__			:  Any  =mincut
			return cut[length - 1]


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