




_lowerCAmelCase						:   Union[str, Any]		=   {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_lowerCAmelCase						:   Optional[Any]		=   ["a", "b", "c", "d", "e"]



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[Any]						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   Tuple	):




 """simple docstring"""







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


if __name__ == "__main__":
  _lowerCAmelCase						:   Union[str, Any]		=   topological_sort("a", [], [])
  print(sort)











from typing import TYPE_CHECKING

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


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   List[str]		=   [
      "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:
  _lowerCAmelCase						:   Tuple		=   [
      "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

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





