






"""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


lowerCamelCase_       			=   logging.getLogger(__name__)







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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    str:
				UpperCAmelCase_  :				Optional[int]      =      super().to_dict()
				for k, v in d.items():
							if isinstance(lowerCAmelCase_			,			lowerCAmelCase_		):
										UpperCAmelCase_  :				Optional[int]      =      v.to_dict()
				return d











"""simple docstring"""
from itertools import count

def        snake_case   (     A__ = 50		):
			UpperCAmelCase_  :				Any      =      [1] * min_block_length

			for n in count(A__		):
						fill_count_functions.append(1		)

						for block_length in range(A__			,n + 1		):
									for block_start in range(n - block_length		):
												fill_count_functions[n] += fill_count_functions[
												    n - block_start - block_length - 1
												]

									fill_count_functions[n] += 1

						if fill_count_functions[n] > 1_00_00_00:
									break

			return n


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



