






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase							:   List[Any]							=  {
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCAmelCase							:   Dict							=  ["DistilBertTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCAmelCase							:   str							=  [
	    "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "DistilBertForMaskedLM",
	    "DistilBertForMultipleChoice",
	    "DistilBertForQuestionAnswering",
	    "DistilBertForSequenceClassification",
	    "DistilBertForTokenClassification",
	    "DistilBertModel",
	    "DistilBertPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCAmelCase							:   Dict							=  [
	    "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFDistilBertForMaskedLM",
	    "TFDistilBertForMultipleChoice",
	    "TFDistilBertForQuestionAnswering",
	    "TFDistilBertForSequenceClassification",
	    "TFDistilBertForTokenClassification",
	    "TFDistilBertMainLayer",
	    "TFDistilBertModel",
	    "TFDistilBertPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCAmelCase							:   Union[str, Any]							=  [
	    "FlaxDistilBertForMaskedLM",
	    "FlaxDistilBertForMultipleChoice",
	    "FlaxDistilBertForQuestionAnswering",
	    "FlaxDistilBertForSequenceClassification",
	    "FlaxDistilBertForTokenClassification",
	    "FlaxDistilBertModel",
	    "FlaxDistilBertPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_distilbert import (
	    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DistilBertConfig,
	    DistilBertOnnxConfig,
	)
	from .tokenization_distilbert import DistilBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_distilbert_fast import DistilBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_distilbert import (
		    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DistilBertForMaskedLM,
		    DistilBertForMultipleChoice,
		    DistilBertForQuestionAnswering,
		    DistilBertForSequenceClassification,
		    DistilBertForTokenClassification,
		    DistilBertModel,
		    DistilBertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_distilbert import (
		    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDistilBertForMaskedLM,
		    TFDistilBertForMultipleChoice,
		    TFDistilBertForQuestionAnswering,
		    TFDistilBertForSequenceClassification,
		    TFDistilBertForTokenClassification,
		    TFDistilBertMainLayer,
		    TFDistilBertModel,
		    TFDistilBertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_distilbert import (
		    FlaxDistilBertForMaskedLM,
		    FlaxDistilBertForMultipleChoice,
		    FlaxDistilBertForQuestionAnswering,
		    FlaxDistilBertForSequenceClassification,
		    FlaxDistilBertForTokenClassification,
		    FlaxDistilBertModel,
		    FlaxDistilBertPreTrainedModel,
		)

else:
	import sys

	__UpperCAmelCase							:   int							=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







__UpperCAmelCase							:   int							=  {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
__UpperCAmelCase							:   List[str]							=  ["a", "b", "c", "d", "e"]





def 					lowercase_ (    __snake_case     :							Optional[Any]	,	__snake_case     :							List[Any]	,	__snake_case     :							Tuple      )     -> Optional[int]:





				'''simple docstring'''


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


if __name__ == "__main__":
	__UpperCAmelCase							:   Tuple							=  topological_sort("a", [], [])
	print(sort)