






"""simple docstring"""



from __future__ import annotations

from queue import Queue

class 			a	:







    def __init__(     self			):
      lowercase				       =			{}







    def      UpperCamelCase_ (     self			):
      for i in self.vertices:
        print(_lowerCamelCase				,						' : '				,						' -> '.join([str(_lowerCamelCase			) for j in self.vertices[i]]			)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase			):
      if from_vertex in self.vertices:
        self.vertices[from_vertex].append(_lowerCamelCase			)
      else:
        lowercase				       =			[to_vertex]







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			set()

      # create a first in first out queue to store all the vertices for BFS
      lowercase				       =			Queue()

      # mark the source node as visited and enqueue it
      visited.add(_lowerCamelCase			)
      queue.put(_lowerCamelCase			)

      while not queue.empty():
        lowercase				       =			queue.get()

        # loop through all adjacent vertex and enqueue it if not yet visited
        for adjacent_vertex in self.vertices[vertex]:
          if adjacent_vertex not in visited:
            queue.put(_lowerCamelCase			)
            visited.add(_lowerCamelCase			)
      return visited


if __name__ == "__main__":
    from doctest import testmod

    testmod(verbose=True)

    _UpperCamelCase						:    List[str]		=						Graph()
    g.add_edge(0, 1)
    g.add_edge(0, 2)
    g.add_edge(1, 2)
    g.add_edge(2, 0)
    g.add_edge(2, 3)
    g.add_edge(3, 3)

    g.print_graph()
    # 0  :  1 -> 2
    # 1  :  2
    # 2  :  0 -> 3
    # 3  :  3

    assert sorted(g.bfs(2)) == [0, 1, 2, 3]





import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter






class     __magic_name__     (							TensorFormatter[Mapping, np.ndarray, Mapping]     ):


 def __init__(    self		:			Optional[int]      ,    lowerCAmelCase__		:			str=None      ,    **lowerCAmelCase__		:			int     )     ->   Optional[Any]:
   super().__init__(features=lowerCAmelCase__     )
   UpperCAmelCase          =	np_array_kwargs


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			Dict     )     ->   Union[str, Any]:
   if isinstance(lowerCAmelCase__      ,    lowerCAmelCase__     ):
     if column and all(
         isinstance(lowerCAmelCase__      ,    np.ndarray     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
       return np.stack(lowerCAmelCase__     )
     else:
       # don't use np.array(column, dtype=object)
       # since it fails in certain cases
       # see https://stackoverflow.com/q/51005699
       UpperCAmelCase          =	np.empty(len(lowerCAmelCase__     )      ,    dtype=lowerCAmelCase__     )
       UpperCAmelCase          =	column
       return out
   return column


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			Union[str, Any]     )     ->   List[Any]:
   if isinstance(lowerCAmelCase__      ,    (str, bytes, type(lowerCAmelCase__     ))     ):
     return value
   elif isinstance(lowerCAmelCase__      ,    (np.character, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.character     ):
     return value
   elif isinstance(lowerCAmelCase__      ,    np.number     ):
     return value

   UpperCAmelCase          =	{}

   if isinstance(lowerCAmelCase__      ,    np.ndarray     ) and np.issubdtype(value.dtype      ,    np.integer     ):
     UpperCAmelCase          =	{"dtype": np.intaa}
   elif isinstance(lowerCAmelCase__      ,    np.ndarray     ) and np.issubdtype(value.dtype      ,    np.floating     ):
     UpperCAmelCase          =	{"dtype": np.floataa}
   elif config.PIL_AVAILABLE and "PIL" in sys.modules:
     import PIL.Image

     if isinstance(lowerCAmelCase__      ,    PIL.Image.Image     ):
       return np.asarray(lowerCAmelCase__      ,    **self.np_array_kwargs     )

   return np.asarray(lowerCAmelCase__      ,    **{**default_dtype, **self.np_array_kwargs}     )


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			str     )     ->   Optional[int]:
   # support for torch, tf, jax etc.
   if config.TORCH_AVAILABLE and "torch" in sys.modules:
     import torch

     if isinstance(lowerCAmelCase__      ,    torch.Tensor     ):
       return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
   if hasattr(lowerCAmelCase__      ,    "__array__"     ) and not isinstance(lowerCAmelCase__      ,    (np.ndarray, np.character, np.number)     ):
     UpperCAmelCase          =	data_struct.__array__()
   # support for nested types like struct of list of struct
   if isinstance(lowerCAmelCase__      ,    np.ndarray     ):
     if data_struct.dtype == object:
       return self._consolidate([self.recursive_tensorize(lowerCAmelCase__     ) for substruct in data_struct]     )
   if isinstance(lowerCAmelCase__      ,    (list, tuple)     ):
     return self._consolidate([self.recursive_tensorize(lowerCAmelCase__     ) for substruct in data_struct]     )
   return self._tensorize(lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			dict     )     ->   Union[str, Any]:
   return map_nested(self._recursive_tensorize      ,    lowerCAmelCase__      ,    map_list=lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			pa.Table     )     ->   Mapping:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_row(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_row(lowerCAmelCase__     )
   return self.recursive_tensorize(lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			pa.Table     )     ->   np.ndarray:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_column(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_column(lowerCAmelCase__      ,    pa_table.column_names[0]     )
   UpperCAmelCase          =	self.recursive_tensorize(lowerCAmelCase__     )
   UpperCAmelCase          =	self._consolidate(lowerCAmelCase__     )
   return column


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			pa.Table     )     ->   Mapping:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_batch(lowerCAmelCase__     )
   UpperCAmelCase          =	self.recursive_tensorize(lowerCAmelCase__     )
   for column_name in batch:
     UpperCAmelCase          =	self._consolidate(batch[column_name]     )
   return batch



