
class    _snake_case :


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Any     		=   None
   lowercase__	: int     		=   None
   lowercase__	: Tuple     		=   graph

   self._normalize_graph(__lowerCamelCase       ,	__lowerCamelCase)
   lowercase__	: Union[str, Any]     		=   len(__lowerCamelCase)
   lowercase__	: str     		=   None


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if sources is int:
     lowercase__	: Optional[int]     		=   [sources]
   if sinks is int:
     lowercase__	: Dict     		=   [sinks]

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

   lowercase__	: List[Any]     		=   sources[0]
   lowercase__	: Dict     		=   sinks[0]

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

     lowercase__	: Dict     		=   len(self.graph) + 1
     for room in self.graph:
       room.insert(0       ,	0)
     self.graph.insert(0       ,	[0] * size)
     for i in sources:
       lowercase__	: Union[str, Any]     		=   max_input_flow
     lowercase__	: Optional[int]     		=   0

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


 def   lowercase__  (       self):


   '''simple docstring'''




   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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   algorithm(self)



class    _snake_case :


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   flow_network
   lowercase__	: int     		=   flow_network.verticesCount
   lowercase__	: str     		=   flow_network.sourceIndex
   lowercase__	: int     		=   flow_network.sinkIndex
   # it's just a reference, so you shouldn't change
   # it in your algorithms, use deep copy before doing that
   lowercase__	: List[Any]     		=   flow_network.graph
   lowercase__	: Union[str, Any]     		=   False


 def   lowercase__  (       self):


   '''simple docstring'''




   if not self.executed:
     self._algorithm()
     lowercase__	: Optional[Any]     		=   True



 def   lowercase__  (       self):


   '''simple docstring'''




   pass



class    _snake_case (						__SCREAMING_SNAKE_CASE				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(__lowerCamelCase)
   # use this to save your result
   lowercase__	: Optional[Any]     		=   -1



 def   lowercase__  (       self):


   '''simple docstring'''




   if not self.executed:
     raise Exception("""You should execute algorithm before using its result!""")

   return self.maximum_flow



class    _snake_case (						__SCREAMING_SNAKE_CASE				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(__lowerCamelCase)

   lowercase__	: Union[str, Any]     		=   [[0] * self.verticies_count for i in range(self.verticies_count)]

   lowercase__	: List[Any]     		=   [0] * self.verticies_count
   lowercase__	: List[Any]     		=   [0] * self.verticies_count


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   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
   lowercase__	: List[Any]     		=   [
       i
       for i in range(self.verticies_count)
       if i != self.source_index and i != self.sink_index
   ]

   # move through list
   lowercase__	: Union[str, Any]     		=   0
   while i < len(__lowerCamelCase):
     lowercase__	: Union[str, Any]     		=   vertices_list[i]
     lowercase__	: Optional[Any]     		=   self.heights[vertex_index]
     self.process_vertex(__lowerCamelCase)
     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(__lowerCamelCase))
       lowercase__	: Tuple     		=   0
     else:
       i += 1

   lowercase__	: Optional[Any]     		=   sum(self.preflow[self.source_index])


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   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(__lowerCamelCase       ,	__lowerCamelCase)

     self.relabel(__lowerCamelCase)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: List[str]     		=   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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   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):
       lowercase__	: Any     		=   self.heights[to_index]

   if min_height is not None:
     lowercase__	: str     		=   min_height + 1


if __name__ == "__main__":
     lowerCamelCase__     :		str        = [0]
     lowerCamelCase__     :		List[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],
     # ]
     lowerCamelCase__     :		Union[str, Any]        = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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











"""simple docstring"""






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

import yaml



class     UpperCAmelCase    (	yaml.SafeLoader  ):



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      [self.constructed_objects[key_node] for key_node, _ in node.value]
     _snake_case   	=      [tuple(__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else key for key in keys]
     _snake_case   	=      Counter(__lowerCamelCase				)
     _snake_case   	=      [key for key in counter if counter[key] > 1]
     if duplicate_keys:
       raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}"""				)





   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Optional[Any]=False				):



     """simple docstring"""
     _snake_case   	=      super().construct_mapping(__lowerCamelCase							,       deep=__lowerCamelCase				)
     self._check_no_duplicates_on_constructed_node(__lowerCamelCase				)
     return mapping




def 			snake_case		(		lowerCAmelCase_	)						->				Tuple[Optional[str], str]:
  _snake_case   	=      list(readme_content.splitlines()	)
  if full_content and full_content[0] == "---" and "---" in full_content[1:]:
    _snake_case   	=      full_content[1:].index('''---'''	) + 1
    _snake_case   	=      '''\n'''.join(full_content[1:sep_idx]	)
    return yamlblock, "\n".join(full_content[sep_idx + 1 :]	)

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



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   # class attributes
   A__		:	int           =  {'''train_eval_index'''}  # train-eval-index in the YAML metadata



   @classmethod
   def    __UpperCAmelCase    (       cls    :	str							,       __lowerCamelCase    :	Path				):



     """simple docstring"""
     with open(__lowerCamelCase							,       encoding='''utf-8'''				) as readme_file:
       _snake_case	,     _snake_case   	=      _split_yaml_from_readme(readme_file.read()				)
     if yaml_string is not None:
       return cls.from_yaml_string(__lowerCamelCase				)
     else:
       return cls()



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	Path				):



     """simple docstring"""
     if path.exists():
       with open(__lowerCamelCase							,       encoding='''utf-8'''				) as readme_file:
         _snake_case   	=      readme_file.read()
     else:
       _snake_case   	=      None
     _snake_case   	=      self._to_readme(__lowerCamelCase				)
     with open(__lowerCamelCase							,       '''w'''							,       encoding='''utf-8'''				) as readme_file:
       readme_file.write(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[str] = None				):



     """simple docstring"""
     if readme_content is not None:
       _snake_case	,     _snake_case   	=      _split_yaml_from_readme(__lowerCamelCase				)
       _snake_case   	=      '''---\n''' + self.to_yaml_string() + '''---\n''' + content
     else:
       _snake_case   	=      '''---\n''' + self.to_yaml_string() + '''---\n'''
     return full_content



   @classmethod
   def    __UpperCAmelCase    (       cls    :	Tuple							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      yaml.load(__lowerCamelCase							,       Loader=_NoDuplicateSafeLoader				) or {}

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





   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     return yaml.safe_dump(
         {
             (key.replace('''_'''							,       '''-'''				) if key in self._FIELDS_WITH_DASHES else key): value
             for key, value in self.items()
         }							,       sort_keys=__lowerCamelCase							,       allow_unicode=__lowerCamelCase							,       encoding='''utf-8'''							,       ).decode('''utf-8'''				)

snake_case   					=				{
    '''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

     snake_case   					=				ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
     ap.add_argument('''readme_filepath''')
     snake_case   					=				ap.parse_args()

     snake_case   					=				Path(args.readme_filepath)
     snake_case   					=				DatasetMetadata.from_readme(readme_filepath)
     print(dataset_metadata)
     dataset_metadata.to_readme(readme_filepath)





