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


__SCREAMING_SNAKE_CASE						:   int         =			logging.getLogger(__name__)



@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class        lowercase_   (					__snake_case		):
       _lowerCamelCase						=		field(default=__snake_case						,			metadata={'help': 'Whether to use SortishSampler or not.'}		)
       _lowerCamelCase						=		field(
           default=__snake_case						,			metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
       _lowerCamelCase						=		field(
           default=__snake_case						,			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						=		field(
           default=__snake_case						,			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						=		field(
           default=__snake_case						,			metadata={
               'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
           }						,			)



       def 	UpperCamelCase	(						self    ):
             _snake_case						:					str    =   super().to_dict()
             for k, v in d.items():
                   if isinstance(lowercase_				,    lowercase_    ):
                         _snake_case						:					Dict    =   v.to_dict()
             return d
from __future__ import annotations
def  snake_case						(__lowercase ,      __lowercase						)	->	float:

						'''simple docstring'''



						_snake_case						:					Any    =   sorted(numsa + numsa						)
						_snake_case    ,_snake_case						:					Any    =   divmod(len(__lowercase						) ,      2						)
						if mod == 1:
												return all_numbers[div]
						else:
												return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			[float(x) for x in input('Enter the elements of first array: ').split()]
				__SCREAMING_SNAKE_CASE						:   List[Any]         =			[float(x) for x in input('Enter the elements of second array: ').split()]
				print(F'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')