


'''simple docstring'''
def      a (     )    ->      Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__  :str				   =					[31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]

					UpperCamelCase__  :Optional[int]				   =					6
					UpperCamelCase__  :List[str]				   =					1
					UpperCamelCase__  :Any				   =					1901

					UpperCamelCase__  :Union[str, Any]				   =					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__  :Dict				   =					day - days_per_month[month - 2]
															elif day > 29 and month == 2:
																				month += 1
																				UpperCamelCase__  :Union[str, Any]				   =					day - 29
										else:
															if day > days_per_month[month - 1]:
																				month += 1
																				UpperCamelCase__  :Optional[Any]				   =					day - days_per_month[month - 2]

										if month > 12:
															year += 1
															UpperCamelCase__  :Union[str, Any]				   =					1

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


if __name__ == "__main__":
		print(solution())



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__snake_case				 =	{
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTNeoForCausalLM''',
		    '''GPTNeoForQuestionAnswering''',
		    '''GPTNeoForSequenceClassification''',
		    '''GPTNeoForTokenClassification''',
		    '''GPTNeoModel''',
		    '''GPTNeoPreTrainedModel''',
		    '''load_tf_weights_in_gpt_neo''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''FlaxGPTNeoForCausalLM''',
		    '''FlaxGPTNeoModel''',
		    '''FlaxGPTNeoPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_neo import (
				    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTNeoForCausalLM,
				    GPTNeoForQuestionAnswering,
				    GPTNeoForSequenceClassification,
				    GPTNeoForTokenClassification,
				    GPTNeoModel,
				    GPTNeoPreTrainedModel,
				    load_tf_weights_in_gpt_neo,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
		import sys

		__snake_case				 =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)