





from __future__ import annotations






class 					lowercase__ :

   '''simple docstring'''


   def __init__(					self,	__magic_name__							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   data
       UpperCamelCase__							:       List[Any]      =   None
       UpperCamelCase__							:       List[str]      =   None




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Node | None   )   ->			Union[str, Any]:  # In Order traversal of the tree
    if tree:
        display(tree.left   )
        print(tree.data   )
        display(tree.right   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Node | None   )   ->			int:
    return 1 + max(depth_of_tree(tree.left   )	,     depth_of_tree(tree.right   )   ) if tree else 0




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Node   )   ->			Optional[Any]:
    if not tree:
        return True
    if tree.left and tree.right:
        return is_full_binary_tree(tree.left   ) and is_full_binary_tree(tree.right   )
    else:
        return not tree.left and not tree.right




def 	lowerCAmelCase_					(						)   ->			Any:  # Main function for testing.
    UpperCamelCase__							:       int      =   Node(1   )
    UpperCamelCase__							:       Dict      =   Node(2   )
    UpperCamelCase__							:       str      =   Node(3   )
    UpperCamelCase__							:       List[Any]      =   Node(4   )
    UpperCamelCase__							:       Dict      =   Node(5   )
    UpperCamelCase__							:       Optional[Any]      =   Node(6   )
    UpperCamelCase__							:       int      =   Node(7   )
    UpperCamelCase__							:       str      =   Node(8   )
    UpperCamelCase__							:       Tuple      =   Node(9   )

    print(is_full_binary_tree(__UpperCAmelCase   )   )
    print(depth_of_tree(__UpperCAmelCase   )   )
    print('''Tree is: '''   )
    display(__UpperCAmelCase   )


if __name__ == "__main__":
  main()








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 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							bool     	=	field(default=__lowerCamelCase    ,       metadata={"help": "Whether to use SortishSampler or not."}       )
   a		:							bool     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}       )
   a		:							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."
           )
       }    ,       )
   a		:							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."
           )
       }    ,       )
   a		:							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    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   super().to_dict()
       for k, v in d.items():
           if isinstance(__magic_name__,	__magic_name__							):
               UpperCamelCase__							:       Tuple      =   v.to_dict()
       return d

