


'''simple docstring'''




from __future__ import annotations

from typing import Any





def 	a_					( lowerCamelCase	:     list				):
   if not postfix_notation:
      return 0

   lowerCAmelCase     =			{'+', '-', '*', '/'}
   lowerCAmelCase     =			[]

   for token in postfix_notation:
      if token in operations:
         lowerCAmelCase     ,  lowerCAmelCase     =			stack.pop(), stack.pop()
         if token == "+":
            stack.append(a + b				)
         elif token == "-":
            stack.append(a - b				)
         elif token == "*":
            stack.append(a * b				)
         else:
            if a * b < 0 and a % b != 0:
               stack.append(a // b + 1				)
            else:
               stack.append(a // b				)
      else:
         stack.append(int(lowerCamelCase				)				)

   return stack.pop()


if __name__ == "__main__":
       import doctest

       doctest.testmod()









'''simple docstring'''




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


__snake_case    						=logging.getLogger(__name__)

@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	bool		=     field(default=__lowercase       ,	metadata={'''help''': '''Whether to use SortishSampler or not.'''}		)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}		)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	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.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	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.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[Union[str, Path, GenerationConfig]]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }       ,	)





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[str]:
        lowerCAmelCase     =			super().to_dict()
        for k, v in d.items():
           if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
              lowerCAmelCase     =			v.to_dict()
        return d





