
import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
class    _UpperCamelCase(   TensorFormatter[Mapping, np.ndarray, Mapping]				):

						def __init__(					self		:			Union[str, Any]	,						_lowerCamelCase		:			Dict=None	,						**_lowerCamelCase		:			str			):
													super().__init__(features=A__			)
													_UpperCAmelCase  :			str	       =		np_array_kwargs

						def 						a__ (					self		:			str	,						_lowerCamelCase		:			Dict			):
													if isinstance(A__	,						A__			):
																				if column and all(
																				    isinstance(A__	,						np.ndarray			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
																											return np.stack(A__			)
																				else:
																											# don't use np.array(column, dtype=object)
																											# since it fails in certain cases
																											# see https://stackoverflow.com/q/51005699
																											_UpperCAmelCase  :			Optional[int]	       =		np.empty(len(A__			)	,						dtype=A__			)
																											_UpperCAmelCase  :			Optional[Any]	       =		column
																											return out
													return column

						def 						a__ (					self		:			Tuple	,						_lowerCamelCase		:			Optional[int]			):
													if isinstance(A__	,						(str, bytes, type(A__			))			):
																				return value
													elif isinstance(A__	,						(np.character, np.ndarray)			) and np.issubdtype(value.dtype	,						np.character			):
																				return value
													elif isinstance(A__	,						np.number			):
																				return value

													_UpperCAmelCase  :			Dict	       =		{}

													if isinstance(A__	,						np.ndarray			) and np.issubdtype(value.dtype	,						np.integer			):
																				_UpperCAmelCase  :			List[Any]	       =		{"dtype": np.intaa}
													elif isinstance(A__	,						np.ndarray			) and np.issubdtype(value.dtype	,						np.floating			):
																				_UpperCAmelCase  :			str	       =		{"dtype": np.floataa}
													elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																				import PIL.Image

																				if isinstance(A__	,						PIL.Image.Image			):
																											return np.asarray(A__	,						**self.np_array_kwargs			)

													return np.asarray(A__	,						**{**default_dtype, **self.np_array_kwargs}			)

						def 						a__ (					self		:			Union[str, Any]	,						_lowerCamelCase		:			Union[str, Any]			):
													# support for torch, tf, jax etc.
													if config.TORCH_AVAILABLE and "torch" in sys.modules:
																				import torch

																				if isinstance(A__	,						torch.Tensor			):
																											return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
													if hasattr(A__	,						"__array__"			) and not isinstance(A__	,						(np.ndarray, np.character, np.number)			):
																				_UpperCAmelCase  :			Optional[int]	       =		data_struct.__array__()
													# support for nested types like struct of list of struct
													if isinstance(A__	,						np.ndarray			):
																				if data_struct.dtype == object:
																											return self._consolidate([self.recursive_tensorize(A__			) for substruct in data_struct]			)
													if isinstance(A__	,						(list, tuple)			):
																				return self._consolidate([self.recursive_tensorize(A__			) for substruct in data_struct]			)
													return self._tensorize(A__			)

						def 						a__ (					self		:			List[Any]	,						_lowerCamelCase		:			dict			):
													return map_nested(self._recursive_tensorize	,						A__	,						map_list=A__			)

						def 						a__ (					self		:			str	,						_lowerCamelCase		:			pa.Table			):
													_UpperCAmelCase  :			Optional[int]	       =		self.numpy_arrow_extractor().extract_row(A__			)
													_UpperCAmelCase  :			int	       =		self.python_features_decoder.decode_row(A__			)
													return self.recursive_tensorize(A__			)

						def 						a__ (					self		:			Optional[Any]	,						_lowerCamelCase		:			pa.Table			):
													_UpperCAmelCase  :			Tuple	       =		self.numpy_arrow_extractor().extract_column(A__			)
													_UpperCAmelCase  :			Union[str, Any]	       =		self.python_features_decoder.decode_column(A__	,						pa_table.column_names[0]			)
													_UpperCAmelCase  :			List[str]	       =		self.recursive_tensorize(A__			)
													_UpperCAmelCase  :			List[str]	       =		self._consolidate(A__			)
													return column






						def 						a__ (					self		:			Dict	,						_lowerCamelCase		:			pa.Table			):
													_UpperCAmelCase  :			List[Any]	       =		self.numpy_arrow_extractor().extract_batch(A__			)
													_UpperCAmelCase  :			Optional[Any]	       =		self.python_features_decoder.decode_batch(A__			)
													_UpperCAmelCase  :			Any	       =		self.recursive_tensorize(A__			)
													for column_name in batch:
																				_UpperCAmelCase  :			Dict	       =		self._consolidate(batch[column_name]			)
													return batch







def  snake_case_(_UpperCamelCase	)				->      int:



  """simple docstring"""






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

