




"""simple docstring"""

from __future__ import annotations

from cmath import sqrt






def 						_snake_case					( _snake_case							: int    ,  _snake_case							: int    ,  _snake_case							: int							)   -> tuple[complex, complex]:






      '''simple docstring'''


      if a == 0:
            raise ValueError('Coefficient \'a\' must not be zero.'							)
      _A        =       b * b - 4 * a * c

      _A        =       (-b + sqrt(_snake_case							)) / (2 * a)
      _A        =       (-b - sqrt(_snake_case							)) / (2 * a)

      return (
          root_a.real if not root_a.imag else root_a,
          root_a.real if not root_a.imag else root_a,
      )






def 						_snake_case					( )   -> str:






      '''simple docstring'''


      _A       ,   _A        =       quadratic_roots(a=5    ,  b=6    ,  c=1							)
      print(F'''The solutions are: {solutiona} and {solutiona}'''							)


if __name__ == "__main__":
       main()







"""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


a         =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__							)
class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 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	:	int		):
       _A        =       super().to_dict()
       for k, v in d.items():
             if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
                   _A        =       v.to_dict()
       return d

