


from __future__ import annotations


def 				lowerCamelCase__				(	A       :      int				,       A       :      int					):




						'''simple docstring'''






						UpperCAmelCase					    =     []
						create_all_state(1				,       A				,       A				,       []				,       A					)
						return result


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




						'''simple docstring'''






						if level == 0:
												total_list.append(current_list[:]					)
												return

						for i in range(A				,       total_number - level + 2					):
												current_list.append(A					)
												create_all_state(i + 1				,       A				,       level - 1				,       A				,       A					)
												current_list.pop()


def 				lowerCamelCase__				(	A       :      list[list[int]]					):




						'''simple docstring'''






						for i in total_list:
												print(*A					)


if __name__ == "__main__":
	_lowercase      :      str            =					4
	_lowercase      :      int            =					2
	_lowercase      :      Tuple            =					generate_all_combinations(n, k)
	print_all_state(total_list)









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase      :      List[str]            =					{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      Dict            =					[
	    """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimeSeriesTransformerForPrediction""",
	    """TimeSeriesTransformerModel""",
	    """TimeSeriesTransformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	_lowercase      :      Tuple            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





