



'''simple docstring'''
snake_case_					:      List[str]    			=    {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
snake_case_					:      Tuple    			=    ['a', 'b', 'c', 'd', 'e']






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Union[str, Any]												=  start
						# add current to visited
						visited.append(UpperCAmelCase_				)
						_UpperCamelCase   :						List[str]												=  edges[current]
						for neighbor in neighbors:
												# if neighbor not in visited, visit
												if neighbor not in visited:
																		_UpperCamelCase   :						Dict												=  topological_sort(UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				)
    # if all neighbors visited add current to sort
						sort.append(UpperCAmelCase_				)
						# if all vertices haven't been visited select a new one to visit
						if len(UpperCAmelCase_				) != len(UpperCAmelCase_				):
												for vertice in vertices:
																		if vertice not in visited:
																								_UpperCamelCase   :						Union[str, Any]												=  topological_sort(UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				)
    # return sort
						return sort


if __name__ == "__main__":
					snake_case_					:      Any    			=    topological_sort('a', [], [])
					print(sort)







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

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


snake_case_					:      Union[str, Any]    			=    {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      str    			=    [
					    'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'AdaptiveEmbedding',
					    'TransfoXLForSequenceClassification',
					    'TransfoXLLMHeadModel',
					    'TransfoXLModel',
					    'TransfoXLPreTrainedModel',
					    'load_tf_weights_in_transfo_xl',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Any    			=    [
					    'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFAdaptiveEmbedding',
					    'TFTransfoXLForSequenceClassification',
					    'TFTransfoXLLMHeadModel',
					    'TFTransfoXLMainLayer',
					    'TFTransfoXLModel',
					    'TFTransfoXLPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
					from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_transfo_xl import (
										    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AdaptiveEmbedding,
										    TransfoXLForSequenceClassification,
										    TransfoXLLMHeadModel,
										    TransfoXLModel,
										    TransfoXLPreTrainedModel,
										    load_tf_weights_in_transfo_xl,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_transfo_xl import (
										    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFAdaptiveEmbedding,
										    TFTransfoXLForSequenceClassification,
										    TFTransfoXLLMHeadModel,
										    TFTransfoXLMainLayer,
										    TFTransfoXLModel,
										    TFTransfoXLPreTrainedModel,
										)

else:
					import sys

					snake_case_					:      Dict    			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


