



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 		_lowercase		(							TensorFormatter[Mapping, np.ndarray, Mapping]						):

   def __init__(   self      :							Optional[Any]				, __lowerCAmelCase      :							List[str]=None				, **__lowerCAmelCase      :							List[str]				)							->					Tuple:


    """simple docstring"""






    super().__init__(features=__lowerCAmelCase				)
    a							=		np_array_kwargs

   def 		A				(   self      :							Optional[Any]				, __lowerCAmelCase      :							int				)							->					Optional[Any]:


    """simple docstring"""






    if isinstance(__lowerCAmelCase				, __lowerCAmelCase				):
     if column and all(
         isinstance(__lowerCAmelCase				, np.ndarray				) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column				):
      return np.stack(__lowerCAmelCase				)
     else:
      # don't use np.array(column, dtype=object)
      # since it fails in certain cases
      # see https://stackoverflow.com/q/51005699
      a							=		np.empty(len(__lowerCAmelCase				)				, dtype=__lowerCAmelCase				)
      a							=		column
      return out
    return column

   def 		A				(   self      :							Optional[int]				, __lowerCAmelCase      :							Dict				)							->					List[Any]:


    """simple docstring"""






    if isinstance(__lowerCAmelCase				, (str, bytes, type(__lowerCAmelCase				))				):
     return value
    elif isinstance(__lowerCAmelCase				, (np.character, np.ndarray)				) and np.issubdtype(value.dtype				, np.character				):
     return value
    elif isinstance(__lowerCAmelCase				, np.number				):
     return value

    a							=		{}

    if isinstance(__lowerCAmelCase				, np.ndarray				) and np.issubdtype(value.dtype				, np.integer				):
     a							=		{"dtype": np.intaa}
    elif isinstance(__lowerCAmelCase				, np.ndarray				) and np.issubdtype(value.dtype				, np.floating				):
     a							=		{"dtype": np.floataa}
    elif config.PIL_AVAILABLE and "PIL" in sys.modules:
     import PIL.Image

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

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

   def 		A				(   self      :							Optional[int]				, __lowerCAmelCase      :							Union[str, Any]				)							->					Any:


    """simple docstring"""






    if config.TORCH_AVAILABLE and "torch" in sys.modules:
     import torch

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

   def 		A				(   self      :							Any				, __lowerCAmelCase      :							dict				)							->					Optional[int]:


    """simple docstring"""






    return map_nested(self._recursive_tensorize				, __lowerCAmelCase				, map_list=__lowerCAmelCase				)

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							pa.Table				)							->					Mapping:


    """simple docstring"""






    a							=		self.numpy_arrow_extractor().extract_row(__lowerCAmelCase				)
    a							=		self.python_features_decoder.decode_row(__lowerCAmelCase				)
    return self.recursive_tensorize(__lowerCAmelCase				)

   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							pa.Table				)							->					np.ndarray:


    """simple docstring"""






    a							=		self.numpy_arrow_extractor().extract_column(__lowerCAmelCase				)
    a							=		self.python_features_decoder.decode_column(__lowerCAmelCase				, pa_table.column_names[0]				)
    a							=		self.recursive_tensorize(__lowerCAmelCase				)
    a							=		self._consolidate(__lowerCAmelCase				)
    return column




   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							pa.Table				)							->					Mapping:


    """simple docstring"""






    a							=		self.numpy_arrow_extractor().extract_batch(__lowerCAmelCase				)
    a							=		self.python_features_decoder.decode_batch(__lowerCAmelCase				)
    a							=		self.recursive_tensorize(__lowerCAmelCase				)
    for column_name in batch:
     a							=		self._consolidate(batch[column_name]				)
    return batch








import os



def _a							(      )      ->		int:







      '''simple docstring'''






      __A			  =							os.path.dirname(os.path.realpath(lowerCamelCase					)					)
      __A			  =							os.path.join(lowerCamelCase   ,							'''triangle.txt'''					)

      with open(lowerCamelCase					) as in_file:
            __A			  =							[[int(lowerCamelCase					) for i in line.split()] for line in in_file]

      while len(lowerCamelCase					) != 1:
            __A			  =							triangle.pop()
            __A			  =							triangle[-1]
            for j in range(len(lowerCamelCase					) - 1					):
                  curr_row[j] += max(last_row[j]   ,							last_row[j + 1]					)
      return triangle[0][0]


if __name__ == "__main__":
       print(solution())





