





UpperCAmelCase_  							=				[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]   )   ->			Tuple:
    UpperCamelCase__							:       str      =   0
    while number:
        # Increased Speed Slightly by checking every 5 digits together.
        sum_of_digits_squared += DIGITS_SQUARED[number % 10_0000]
        number //= 10_0000

    return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
UpperCAmelCase_  							=				[None] * 1000_0000
UpperCAmelCase_  							=				True
UpperCAmelCase_  							=				False




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]   )   ->			List[str]:

    if CHAINS[number - 1] is not None:
        return CHAINS[number - 1]  # type: ignore

    UpperCamelCase__							:       Tuple      =   chain(next_number(_snake_case   )   )
    UpperCamelCase__							:       int      =   number_chain

    while number < 1000_0000:
        UpperCamelCase__							:       Any      =   number_chain
        number *= 10

    return number_chain




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict = 1000_0000   )   ->			List[str]:
    for i in range(1	,     _snake_case   ):
        if CHAINS[i] is None:
            chain(i + 1   )

    return CHAINS[:number].count(_snake_case   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(F'''{solution() = }''')








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


UpperCAmelCase_  							=				logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__       )
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							bool     	=	field(default=__lowerCamelCase    ,       metadata={"help": "Whether to use SortishSampler or not."}       )
   a		:							bool     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}       )
   a		:							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."
           )
       }    ,       )
   a		:							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."
           )
       }    ,       )
   a		:							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    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   super().to_dict()
       for k, v in d.items():
           if isinstance(__magic_name__,	__magic_name__							):
               UpperCamelCase__							:       Tuple      =   v.to_dict()
       return d

