






def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[str]       ):
	snake_case__			:				int       =  [0] * len(_lowerCamelCase       )
	snake_case__			:				Optional[int]       =  []
	snake_case__			:				Any       =  []
	snake_case__			:				Tuple       =  0

	for values in graph.values():
		for i in values:
			indegree[i] += 1

	for i in range(len(_lowerCamelCase       )       ):
		if indegree[i] == 0:
			queue.append(_lowerCamelCase       )

	while queue:
		snake_case__			:				str       =  queue.pop(0       )
		cnt += 1
		topo.append(_lowerCamelCase       )
		for x in graph[vertex]:
			indegree[x] -= 1
			if indegree[x] == 0:
				queue.append(_lowerCamelCase       )

	if cnt != len(_lowerCamelCase       ):
		print("Cycle exists"       )
	else:
		print(_lowerCamelCase       )


# Adjacency List of Graph
__lowerCamelCase :      Tuple										=    {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}





topological_sort(graph)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase :      Union[str, Any]										=				{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      Tuple										=				[
	    """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

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



