


UpperCAmelCase   :    Any     =		[0, 2, 4, 6, 8]
UpperCAmelCase   :    Optional[Any]     =		[1, 3, 5, 7, 9]






def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						list[int]  ,							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			if remaining_length == 0:
						if digits[0] == 0 or digits[-1] == 0:
									return 0

						for i in range(length // 2 - 1  ,							-1  ,							-1      ):
									remainder += digits[i] + digits[length - i - 1]

									if remainder % 2 == 0:
												return 0

									remainder //= 10

						return 1

			if remaining_length == 1:
						if remainder % 2 == 0:
									return 0

						lowerCamelCase    		=  0
						for digit in range(10      ):
									lowerCamelCase    		=  digit
									result += reversible_numbers(
									    0  ,							(remainder + 2 * digit) // 10  ,							lowerCamelCase__  ,							lowerCamelCase__      )
						return result

			lowerCamelCase    		=  0
			for digita in range(10      ):
						lowerCamelCase    		=  digita

						if (remainder + digita) % 2 == 0:
									lowerCamelCase    		=  ODD_DIGITS
						else:
									lowerCamelCase    		=  EVEN_DIGITS

						for digita in other_parity_digits:
									lowerCamelCase    		=  digita
									result += reversible_numbers(
									    remaining_length - 2  ,							(remainder + digita + digita) // 10  ,							lowerCamelCase__  ,							lowerCamelCase__  ,							)
			return result






def 	__lowerCamelCase						(							lowerCamelCase__		:						int = 9      ):





			'''simple docstring'''




			lowerCamelCase    		=  0
			for length in range(1  ,							max_power + 1      ):
						result += reversible_numbers(lowerCamelCase__  ,							0  ,							[0] * length  ,							lowerCamelCase__      )
			return result


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





import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase   :    Optional[Any]     =		logging.getLogger(__name__)







@dataclass
class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[float]			       =		field(
		    default=0.0			,							metadata={"help": "The label smoothing epsilon to apply (if not zero)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "Whether to SortishSamler or not."}  )
		UpperCamelCase     :					bool			       =		field(
		    default=a_			,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "whether to use adafactor"}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Encoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Decoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(default=a_			,							metadata={"help": "Dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Attention dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[str]			       =		field(
		    default="linear"			,							metadata={"help": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()  )}'''}			,							)

