




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


UpperCAmelCase_									=	logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__)
class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	bool    		=   field(default=__lowerCamelCase ,     metadata={'help': 'Whether to use SortishSampler or not.'})
      UpperCAmelCase__      :	bool    		=   field(
          default=__lowerCamelCase ,     metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'})
      UpperCAmelCase__      :	Optional[int]    		=   field(
          default=__lowerCamelCase ,     metadata={
              'help': (
                  'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
                  'to the `max_length` value of the model configuration.'
              )
          } ,     )
      UpperCAmelCase__      :	Optional[int]    		=   field(
          default=__lowerCamelCase ,     metadata={
              'help': (
                  'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
                  'to the `num_beams` value of the model configuration.'
              )
          } ,     )
      UpperCAmelCase__      :	Optional[Union[str, Path, GenerationConfig]]    		=   field(
          default=__lowerCamelCase ,     metadata={
              'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
          } ,     )
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						super().to_dict()
             for k, v in d.items():
                    if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                           __lowerCamelCase   =						v.to_dict()
             return d







from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass





@dataclass
class     lowerCamelCase__:
      UpperCAmelCase__      :	int
      UpperCAmelCase__      :	TreeNode | None    		=   None
      UpperCAmelCase__      :	TreeNode | None    		=   None



UpperCAmelCase_									=	namedtuple('CoinsDistribResult', 'moves excess')




def 					lowerCamelCase__       (						A__       :					TreeNode | None	):





       '''simple docstring'''



       if root is None:
              return 0

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

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

       def count_coins(A__       :					TreeNode | None	) -> int:
              if node is None:
                     return 0

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

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

       # Main calculation
       def get_distrib(A__       :					TreeNode | None	) -> CoinsDistribResult:

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

              __lowerCamelCase,   __lowerCamelCase   =						get_distrib(node.left	)
              __lowerCamelCase,   __lowerCamelCase   =						get_distrib(node.right	)

              __lowerCamelCase   =						1 - left_distrib_excess
              __lowerCamelCase   =						1 - right_distrib_excess

              __lowerCamelCase   =						(
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(A__	)
                  + abs(A__	)
              )
              __lowerCamelCase   =						node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(A__  ,			A__	)

       return get_distrib(A__	)[0]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

