class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       str  ,     __magic_name__ :       int  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Union[str, Any]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  graph

        self._normalize_graph(__magic_name__  ,     __magic_name__   )
        SCREAMING_SNAKE_CASE_          =  len(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  None







  def    __A     (							self :       int  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Dict   )  ->	Union[str, Any]:
        if sources is int:
              SCREAMING_SNAKE_CASE_          =  [sources]
        if sinks is int:
              SCREAMING_SNAKE_CASE_          =  [sinks]

        if len(__magic_name__   ) == 0 or len(__magic_name__   ) == 0:
              return

        SCREAMING_SNAKE_CASE_          =  sources[0]
        SCREAMING_SNAKE_CASE_          =  sinks[0]

        # make fake vertex if there are more
        # than one source or sink
        if len(__magic_name__   ) > 1 or len(__magic_name__   ) > 1:
              SCREAMING_SNAKE_CASE_          =  0
              for i in sources:
                    max_input_flow += sum(self.graph[i]   )

              SCREAMING_SNAKE_CASE_          =  len(self.graph   ) + 1
              for room in self.graph:
                    room.insert(0  ,     0   )
              self.graph.insert(0  ,     [0] * size   )
              for i in sources:
                    SCREAMING_SNAKE_CASE_          =  max_input_flow
              SCREAMING_SNAKE_CASE_          =  0

              SCREAMING_SNAKE_CASE_          =  len(self.graph   ) + 1
              for room in self.graph:
                    room.append(0   )
              self.graph.append([0] * size   )
              for i in sinks:
                    SCREAMING_SNAKE_CASE_          =  max_input_flow
              SCREAMING_SNAKE_CASE_          =  size - 1







  def    __A     (							self :       int   )  ->	Tuple:
        if self.maximum_flow_algorithm is None:
              raise Exception("You need to set maximum flow algorithm before."   )
        if self.source_index is None or self.sink_index is None:
              return 0

        self.maximum_flow_algorithm.execute()
        return self.maximum_flow_algorithm.getMaximumFlow()







  def    __A     (							self :       str  ,     __magic_name__ :       int   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  algorithm(self   )



class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       Tuple  ,     __magic_name__ :       List[str]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  flow_network
        SCREAMING_SNAKE_CASE_          =  flow_network.verticesCount
        SCREAMING_SNAKE_CASE_          =  flow_network.sourceIndex
        SCREAMING_SNAKE_CASE_          =  flow_network.sinkIndex
        # it's just a reference, so you shouldn't change
        # it in your algorithms, use deep copy before doing that
        SCREAMING_SNAKE_CASE_          =  flow_network.graph
        SCREAMING_SNAKE_CASE_          =  False







  def    __A     (							self :       Optional[Any]   )  ->	str:
        if not self.executed:
              self._algorithm()
              SCREAMING_SNAKE_CASE_          =  True







  def    __A     (							self :       Union[str, Any]   )  ->	Optional[Any]:
        pass



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       Union[str, Any]  ,     __magic_name__ :       List[Any]   )  ->	str:
        super().__init__(__magic_name__   )
        # use this to save your result
        SCREAMING_SNAKE_CASE_          =  -1







  def    __A     (							self :       Union[str, Any]   )  ->	Dict:
        if not self.executed:
              raise Exception("You should execute algorithm before using its result!"   )

        return self.maximum_flow



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       Dict  ,     __magic_name__ :       List[str]   )  ->	Optional[int]:
        super().__init__(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  [[0] * self.verticies_count for i in range(self.verticies_count   )]

        SCREAMING_SNAKE_CASE_          =  [0] * self.verticies_count
        SCREAMING_SNAKE_CASE_          =  [0] * self.verticies_count







  def    __A     (							self :       Optional[int]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  self.verticies_count

        # push some substance to graph
        for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]   ):
              self.preflow[self.source_index][nextvertex_index] += bandwidth
              self.preflow[nextvertex_index][self.source_index] -= bandwidth
              self.excesses[nextvertex_index] += bandwidth

        # Relabel-to-front selection rule
        SCREAMING_SNAKE_CASE_          =  [
            i
            for i in range(self.verticies_count   )
            if i != self.source_index and i != self.sink_index
        ]

        # move through list
        SCREAMING_SNAKE_CASE_          =  0
        while i < len(__magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  vertices_list[i]
              SCREAMING_SNAKE_CASE_          =  self.heights[vertex_index]
              self.process_vertex(__magic_name__   )
              if self.heights[vertex_index] > previous_height:
                    # if it was relabeled, swap elements
                    # and start from 0 index
                    vertices_list.insert(0  ,     vertices_list.pop(__magic_name__   )   )
                    SCREAMING_SNAKE_CASE_          =  0
              else:
                    i += 1

        SCREAMING_SNAKE_CASE_          =  sum(self.preflow[self.source_index]   )







  def    __A     (							self :       int  ,     __magic_name__ :       Union[str, Any]   )  ->	List[str]:
        while self.excesses[vertex_index] > 0:
              for neighbour_index in range(self.verticies_count   ):
                    # if it's neighbour and current vertex is higher
                    if (
                        self.graph[vertex_index][neighbour_index]
                        - self.preflow[vertex_index][neighbour_index]
                        > 0
                        and self.heights[vertex_index] > self.heights[neighbour_index]
                    ):
                          self.push(__magic_name__  ,     __magic_name__   )

              self.relabel(__magic_name__   )







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  min(
            self.excesses[from_index]  ,     self.graph[from_index][to_index] - self.preflow[from_index][to_index]  ,     )
        self.preflow[from_index][to_index] += preflow_delta
        self.preflow[to_index][from_index] -= preflow_delta
        self.excesses[from_index] -= preflow_delta
        self.excesses[to_index] += preflow_delta







  def    __A     (							self :       List[str]  ,     __magic_name__ :       Optional[Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  None
        for to_index in range(self.verticies_count   ):
              if (
                  self.graph[vertex_index][to_index]
                  - self.preflow[vertex_index][to_index]
                  > 0
              ) and (min_height is None or self.heights[to_index] < min_height):
                    SCREAMING_SNAKE_CASE_          =  self.heights[to_index]

        if min_height is not None:
              SCREAMING_SNAKE_CASE_          =  min_height + 1


if __name__ == "__main__":
      A     :  Any					     =       [0]
      A     :  str					     =       [3]
      # graph = [
      #     [0, 0, 4, 6, 0, 0],
      #     [0, 0, 5, 2, 0, 0],
      #     [0, 0, 0, 0, 4, 4],
      #     [0, 0, 0, 0, 6, 6],
      #     [0, 0, 0, 0, 0, 0],
      #     [0, 0, 0, 0, 0, 0],
      # ]
      A     :  List[str]					     =       [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

      # prepare our network
      A     :  Dict					     =       FlowNetwork(graph, entrances, exits)
      # set algorithm
      flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
      # and calculate
      A     :  str					     =       flow_network.find_maximum_flow()

      print(f"maximum flow is {maximum_flow}")



from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml


class   lowerCamelCase					(yaml.SafeLoader					):
  """simple docstring"""







  def    __A     (							self :       str  ,     __magic_name__ :       str   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  [self.constructed_objects[key_node] for key_node, _ in node.value]
        SCREAMING_SNAKE_CASE_          =  [tuple(__magic_name__   ) if isinstance(__magic_name__  ,     __magic_name__   ) else key for key in keys]
        SCREAMING_SNAKE_CASE_          =  Counter(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  [key for key in counter if counter[key] > 1]
        if duplicate_keys:
              raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''   )







  def    __A     (							self :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]=False   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  super().construct_mapping(__magic_name__  ,     deep=__magic_name__   )
        self._check_no_duplicates_on_constructed_node(__magic_name__   )
        return mapping



def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  list(readme_content.splitlines()				)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            SCREAMING_SNAKE_CASE_          =  full_content[1:].index("---"				) + 1
            SCREAMING_SNAKE_CASE_          =  "\n".join(full_content[1:sep_idx]				)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]				)

      return None, "\n".join(__UpperCamelCase				)


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						{'''train_eval_index'''}  # train-eval-index in the YAML metadata







  @classmethod
  def    __A     (							cls :       Dict  ,     __magic_name__ :       Path   )  ->	"DatasetMetadata":
        with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(readme_file.read()   )
        if yaml_string is not None:
              return cls.from_yaml_string(__magic_name__   )
        else:
              return cls()







  def    __A     (							self :       str  ,     __magic_name__ :       Path   )  ->	List[str]:
        if path.exists():
              with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
                    SCREAMING_SNAKE_CASE_          =  readme_file.read()
        else:
              SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  self._to_readme(__magic_name__   )
        with open(__magic_name__  ,     "w"  ,     encoding="utf-8"   ) as readme_file:
              readme_file.write(__magic_name__   )







  def    __A     (							self :       Any  ,     __magic_name__ :       Optional[str] = None   )  ->	str:
        if readme_content is not None:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n" + content
        else:
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n"
        return full_content







  @classmethod
  def    __A     (							cls :       List[Any]  ,     __magic_name__ :       str   )  ->	"DatasetMetadata":
        SCREAMING_SNAKE_CASE_          =  yaml.load(__magic_name__  ,     Loader=_NoDuplicateSafeLoader   ) or {}

        # Convert the YAML keys to DatasetMetadata fields
        SCREAMING_SNAKE_CASE_          =  {
            (key.replace("-"  ,     "_"   ) if key.replace("-"  ,     "_"   ) in cls._FIELDS_WITH_DASHES else key): value
            for key, value in metadata_dict.items()
        }
        return cls(**__magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	str:
        return yaml.safe_dump(
            {
                (key.replace("_"  ,     "-"   ) if key in self._FIELDS_WITH_DASHES else key): value
                for key, value in self.items()
            }  ,     sort_keys=__magic_name__  ,     allow_unicode=__magic_name__  ,     encoding="utf-8"  ,     ).decode("utf-8"   )



A     :  List[Any]					     =       {
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      A     :  Optional[Any]					     =       ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
      ap.add_argument("readme_filepath")
      A     :  Union[str, Any]					     =       ap.parse_args()

      A     :  Union[str, Any]					     =       Path(args.readme_filepath)
      A     :  List[Any]					     =       DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)


