



def snake_case__	(      )		->				Any:
		UpperCAmelCase_						       =					[31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]

		UpperCAmelCase_						       =					6
		UpperCAmelCase_						       =					1
		UpperCAmelCase_						       =					1901

		UpperCAmelCase_						       =					0

		while year < 2001:
				day += 7

				if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
						if day > days_per_month[month - 1] and month != 2:
								month += 1
								UpperCAmelCase_						       =					day - days_per_month[month - 2]
						elif day > 29 and month == 2:
								month += 1
								UpperCAmelCase_						       =					day - 29
				else:
						if day > days_per_month[month - 1]:
								month += 1
								UpperCAmelCase_						       =					day - days_per_month[month - 2]

				if month > 12:
						year += 1
						UpperCAmelCase_						       =					1

				if year < 2001 and day == 1:
						sundays += 1
		return sundays


if __name__ == "__main__":
			print(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


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





@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   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      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   super().to_dict()
       for k, v in d.items():
           if isinstance(__lowerCamelCase     , __lowerCamelCase			):
               SCREAMING_SNAKE_CASE										=   v.to_dict()
       return d