




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 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	bool	     =      field(default=lowerCAmelCase__    ,				metadata={"""help""": """Whether to use SortishSampler or not."""}     )
    lowerCamelCase_      :	bool	     =      field(
        default=lowerCAmelCase__    ,				metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}     )
    lowerCamelCase_      :	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."""
            )
        }    ,				)
    lowerCamelCase_      :	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."""
            )
        }    ,				)
    lowerCamelCase_      :	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     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      List[Any]			 =					super().to_dict()
           for k, v in d.items():
                  if isinstance(UpperCamelCase__     ,  UpperCamelCase__     ):
                         lowerCamelCase   :      Dict			 =					v.to_dict()
           return d














import math
def 					snake_case_						(   lowerCAmelCase_				:  int					):

							if 1 < number < 4:
														# 2 and 3 are primes
														return True
							elif number < 2 or number % 2 == 0 or number % 3 == 0:
														# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
														return False

							# All primes number are in format of 6k +/- 1
							for i in range(5				,				int(math.sqrt(lowerCAmelCase_					) + 1					)				,				6					):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True
def 					snake_case_						(   lowerCAmelCase_				:  int = 10001					):

							try:
														__lowercase					:							Optional[int]			=   int(lowerCAmelCase_					)
							except (TypeError, ValueError):
														raise TypeError("""Parameter nth must be int or castable to int."""					) from None
							if nth <= 0:
														raise ValueError("""Parameter nth must be greater than or equal to one."""					)
							__lowercase					:							list[int]			=   []
							__lowercase					:							str			=   2
							while len(lowerCAmelCase_					) < nth:
														if is_prime(lowerCAmelCase_					):
																					primes.append(lowerCAmelCase_					)
																					num += 1
														else:
																					num += 1
							return primes[len(lowerCAmelCase_					) - 1]


if __name__ == "__main__":
	print(f'''{solution() = }''')