




def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Optional[Any]:



       """simple docstring"""

       A__							=   set()
       # edges = list of graph's edges
       A__							=   get_edges(SCREAMING_SNAKE_CASE_						)

       # While there are still elements in edges list, take an arbitrary edge
       # (from_node, to_node) and add his extremity to chosen_vertices and then
       # remove all arcs adjacent to the from_node and to_node
       while edges:
              A__			,							A__							=   edges.pop()
              chosen_vertices.add(SCREAMING_SNAKE_CASE_						)
              chosen_vertices.add(SCREAMING_SNAKE_CASE_						)
              for edge in edges.copy():
                     if from_node in edge or to_node in edge:
                            edges.discard(SCREAMING_SNAKE_CASE_						)
       return chosen_vertices







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Optional[int]:



       """simple docstring"""

       A__							=   set()
       for from_node, to_nodes in graph.items():
              for to_node in to_nodes:
                     edges.add((from_node, to_node)						)
       return edges


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_lowerCamelCase     :		Optional[Any]								=       {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Any								=       [
      """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TrOCRForCausalLM""",
      """TrOCRPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
  from .processing_trocr import TrOCRProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
  import sys

  _lowerCamelCase     :		List[str]								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


