"""simple docstring"""




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]	)       ->      Optional[Any]:
  def wrapper(*_lowerCamelCase						:       Any			,**_lowerCamelCase						:       List[str]	):
    _lowerCAmelCase						:       Any  			=     timeit.default_timer()
    _lowerCAmelCase						:       str  			=     func(*_lowerCamelCase			,**_lowerCamelCase	)
    _lowerCAmelCase						:       Tuple  			=     timeit.default_timer() - starttime
    return delta

  _lowerCAmelCase						:       Union[str, Any]  			=     func.__name__

  return wrapper





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       dict			,_lowerCamelCase						:       Optional[Any]=100			,_lowerCamelCase						:       Union[str, Any]=None	)       ->      Union[str, Any]:
  _lowerCAmelCase						:       Optional[Any]  			=     []
  _lowerCAmelCase						:       str  			=     seq_shapes or {}
  for i in range(_lowerCamelCase	):
    _lowerCAmelCase						:       List[Any]  			=     {}
    for col_id, (k, v) in enumerate(features.items()	):
      if isinstance(_lowerCamelCase			,_ArrayXD	):
        _lowerCAmelCase						:       Union[str, Any]  			=     np.random.rand(*v.shape	).astype(v.dtype	)
      elif isinstance(_lowerCamelCase			,datasets.Value	):
        if v.dtype == "string":
          _lowerCAmelCase						:       Union[str, Any]  			=     """The small grey turtle was surprisingly fast when challenged."""
        else:
          _lowerCAmelCase						:       Union[str, Any]  			=     np.random.randint(10			,size=1	).astype(v.dtype	).item()
      elif isinstance(_lowerCamelCase			,datasets.Sequence	):
        while isinstance(_lowerCamelCase			,datasets.Sequence	):
          _lowerCAmelCase						:       List[Any]  			=     v.feature
        _lowerCAmelCase						:       Optional[Any]  			=     seq_shapes[k]
        _lowerCAmelCase						:       Union[str, Any]  			=     np.random.rand(*_lowerCamelCase	).astype(v.dtype	)
      _lowerCAmelCase						:       Union[str, Any]  			=     data

    dummy_data.append((i, example)	)

  return dummy_data






def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Any			,_lowerCamelCase						:       str=100			,_lowerCamelCase						:       List[str]=None	)       ->      List[str]:
  _lowerCAmelCase						:       Dict  			=     generate_examples(_lowerCamelCase			,num_examples=_lowerCamelCase			,seq_shapes=_lowerCamelCase	)

  with ArrowWriter(features=_lowerCamelCase			,path=_lowerCamelCase	) as writer:
    for key, record in dummy_data:
      _lowerCAmelCase						:       List[Any]  			=     features.encode_example(_lowerCamelCase	)
      writer.write(_lowerCamelCase	)

    _lowerCAmelCase			,	_lowerCAmelCase						:       Dict  			=     writer.finalize()

  if not num_final_examples == num_examples:
    raise ValueError(
        f"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."	)

  _lowerCAmelCase						:       Optional[Any]  			=     datasets.Dataset.from_file(filename=_lowerCamelCase			,info=datasets.DatasetInfo(features=_lowerCamelCase	)	)

  return dataset





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any	)       ->      List[Any]:  # noqa: E741
  _lowerCAmelCase						:       Optional[int]  			=     len(_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     0
  _lowerCAmelCase						:       Any  			=     [0] * n
  _lowerCAmelCase						:       str  			=     [False] * n
  _lowerCAmelCase						:       str  			=     [False] * n

  def dfs(_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       str	):
    if parent == root:
      out_edge_count += 1
    _lowerCAmelCase						:       Any  			=     True
    _lowerCAmelCase						:       int  			=     at

    for to in l[at]:
      if to == parent:
        pass
      elif not visited[to]:
        _lowerCAmelCase						:       Union[str, Any]  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
        _lowerCAmelCase						:       Optional[int]  			=     min(low[at]			,low[to]	)

        # AP found via bridge
        if at < low[to]:
          _lowerCAmelCase						:       int  			=     True
        # AP found via cycle
        if at == low[to]:
          _lowerCAmelCase						:       Tuple  			=     True
      else:
        _lowerCAmelCase						:       Union[str, Any]  			=     min(low[at]			,_lowerCamelCase	)
    return out_edge_count

  for i in range(_lowerCamelCase	):
    if not visited[i]:
      _lowerCAmelCase						:       int  			=     0
      _lowerCAmelCase						:       Dict  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,-1			,_lowerCamelCase	)
      _lowerCAmelCase						:       List[str]  			=     out_edge_count > 1

  for x in range(len(_lowerCamelCase	)	):
    if is_art[x] is True:
      print(_lowerCamelCase	)


# Adjacency list of graph
_a      :		Optional[Any]           =   {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}






compute_ap(data)




