





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
    import torch

class 		SCREAMING_SNAKE_CASE__				(       TensorFormatter[Mapping, 'torch.Tensor', Mapping]  ):


      def __init__(self       :      Dict				,			a__       :      Union[str, Any]=None				,			**a__       :      Tuple      ):





         """simple docstring"""



         super().__init__(features=a__      )
         __snake_case							       =   torch_tensor_kwargs
         import torch  # noqa import torch at initialization


      def    a (self       :      Any				,			a__       :      Tuple      ):





         """simple docstring"""



         import torch

         if isinstance(a__				,			a__      ) and column:
            if all(
                isinstance(a__				,			torch.Tensor      ) and x.shape == column[0].shape and x.dtype == column[0].dtype
                for x in column      ):
               return torch.stack(a__      )
         return column


      def    a (self       :      Union[str, Any]				,			a__       :      Dict      ):





         """simple docstring"""



         import torch

         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.tolist()

         __snake_case							       =   {}

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

            if isinstance(a__				,			PIL.Image.Image      ):
               __snake_case							       =   np.asarray(a__      )
         return torch.tensor(a__				,			**{**default_dtype, **self.torch_tensor_kwargs}      )


      def    a (self       :      Any				,			a__       :      Union[str, Any]      ):





         """simple docstring"""



         import torch

         # support for torch, tf, jax etc.
         if hasattr(a__				,			'''__array__'''      ) and not isinstance(a__				,			torch.Tensor      ):
            __snake_case							       =   data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(a__				,			np.ndarray      ):
            if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
               return self._consolidate([self.recursive_tensorize(a__      ) for substruct in data_struct]      )
         elif isinstance(a__				,			(list, tuple)      ):
            return self._consolidate([self.recursive_tensorize(a__      ) for substruct in data_struct]      )
         return self._tensorize(a__      )


      def    a (self       :      str				,			a__       :      dict      ):





         """simple docstring"""



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


      def    a (self       :      List[Any]				,			a__       :      pa.Table      ):





         """simple docstring"""



         __snake_case							       =   self.numpy_arrow_extractor().extract_row(a__      )
         __snake_case							       =   self.python_features_decoder.decode_row(a__      )
         return self.recursive_tensorize(a__      )


      def    a (self       :      str				,			a__       :      pa.Table      ):





         """simple docstring"""



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


      def    a (self       :      List[Any]				,			a__       :      pa.Table      ):





         """simple docstring"""



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



from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class __snake_case		:
    _a		:						int
    _a		:						TreeNode | None=				None
    _a		:						TreeNode | None=				None






lowercase  :					Dict						    =							namedtuple("""CoinsDistribResult""", """moves excess""")







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    int:

       if root is None:
              return 0

       # Validation
       def count_nodes(SCREAMING_SNAKE_CASE__				) -> int:
              if node is None:
                     return 0

              return count_nodes(node.left				) + count_nodes(node.right				) + 1

       def count_coins(SCREAMING_SNAKE_CASE__				) -> int:
              if node is None:
                     return 0

              return count_coins(node.left				) + count_coins(node.right				) + node.data

       if count_nodes(SCREAMING_SNAKE_CASE__				) != count_coins(SCREAMING_SNAKE_CASE__				):
              raise ValueError("""The nodes number should be same as the number of coins"""				)

       # Main calculation
       def get_distrib(SCREAMING_SNAKE_CASE__				) -> CoinsDistribResult:

              if node is None:
                     return CoinsDistribResult(0					,	1				)

              lowercase	,			lowercase							:     int       	=      get_distrib(node.left				)
              lowercase	,			lowercase							:     List[Any]       	=      get_distrib(node.right				)

              lowercase							:     Optional[Any]       	=      1 - left_distrib_excess
              lowercase							:     Union[str, Any]       	=      1 - right_distrib_excess

              lowercase							:     List[Any]       	=      (
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(SCREAMING_SNAKE_CASE__				)
                  + abs(SCREAMING_SNAKE_CASE__				)
              )
              lowercase							:     Any       	=      node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       return get_distrib(SCREAMING_SNAKE_CASE__				)[0]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
