
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
     import torch



class       _snake_case   (	TensorFormatter[Mapping, 'torch.Tensor', Mapping]							):


 def __init__(					self					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(features=SCREAMING_SNAKE_CASE_)
   lowercase__  :   Tuple    		=      torch_tensor_kwargs
   import torch  # noqa import torch at initialization


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   import torch

   if isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_) and column:
     if all(
         isinstance(SCREAMING_SNAKE_CASE_					,  torch.Tensor) and x.shape == column[0].shape and x.dtype == column[0].dtype
         for x in column):
       return torch.stack(SCREAMING_SNAKE_CASE_)
   return column


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   import torch

   if isinstance(SCREAMING_SNAKE_CASE_					,  (str, bytes, type(SCREAMING_SNAKE_CASE_))):
     return value
   elif isinstance(SCREAMING_SNAKE_CASE_					,  (np.character, np.ndarray)) and np.issubdtype(value.dtype					,  np.character):
     return value.tolist()

   lowercase__  :   Optional[Any]    		=      {}

   if isinstance(SCREAMING_SNAKE_CASE_					,  (np.number, np.ndarray)) and np.issubdtype(value.dtype					,  np.integer):
     lowercase__  :   List[str]    		=      {"""dtype""": torch.intaa}
   elif isinstance(SCREAMING_SNAKE_CASE_					,  (np.number, np.ndarray)) and np.issubdtype(value.dtype					,  np.floating):
     lowercase__  :   Optional[int]    		=      {"""dtype""": torch.floataa}
   elif config.PIL_AVAILABLE and "PIL" in sys.modules:
     import PIL.Image

     if isinstance(SCREAMING_SNAKE_CASE_					,  PIL.Image.Image):
       lowercase__  :   str    		=      np.asarray(SCREAMING_SNAKE_CASE_)
   return torch.tensor(SCREAMING_SNAKE_CASE_					,  **{**default_dtype, **self.torch_tensor_kwargs})


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   import torch

   # support for torch, tf, jax etc.
   if hasattr(SCREAMING_SNAKE_CASE_					,  """__array__""") and not isinstance(SCREAMING_SNAKE_CASE_					,  torch.Tensor):
     lowercase__  :   List[str]    		=      data_struct.__array__()
   # support for nested types like struct of list of struct
   if isinstance(SCREAMING_SNAKE_CASE_					,  np.ndarray):
     if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
       return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE_) for substruct in data_struct])
   elif isinstance(SCREAMING_SNAKE_CASE_					,  (list, tuple)):
     return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE_) for substruct in data_struct])
   return self._tensorize(SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return map_nested(self._recursive_tensorize					,  SCREAMING_SNAKE_CASE_					,  map_list=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      self.numpy_arrow_extractor().extract_row(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Dict    		=      self.python_features_decoder.decode_row(SCREAMING_SNAKE_CASE_)
   return self.recursive_tensorize(SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      self.numpy_arrow_extractor().extract_column(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Optional[Any]    		=      self.python_features_decoder.decode_column(SCREAMING_SNAKE_CASE_					,  pa_table.column_names[0])
   lowercase__  :   Union[str, Any]    		=      self.recursive_tensorize(SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[str]    		=      self._consolidate(SCREAMING_SNAKE_CASE_)
   return column



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   str    		=      self.numpy_arrow_extractor().extract_batch(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Optional[int]    		=      self.python_features_decoder.decode_batch(SCREAMING_SNAKE_CASE_)
   lowercase__  :   str    		=      self.recursive_tensorize(SCREAMING_SNAKE_CASE_)
   for column_name in batch:
     lowercase__  :   Union[str, Any]    		=      self._consolidate(batch[column_name])
   return batch








lowerCamelCase__    :		str          =   [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_   )		->	str:


  '''simple docstring'''






  lowercase__  :   Any    		=      [False] * len(lowercase_   )
  lowercase__  :   List[Any]    		=      [s]
  lowercase__  :   int    		=      True

  while queue:
    lowercase__  :   Dict    		=      queue.pop(0   )
    for ind in range(len(graph[u]   )   ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(lowercase_   )
        lowercase__  :   Optional[Any]    		=      True
        lowercase__  :   int    		=      u

  return visited[t]

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	int:


  '''simple docstring'''






  lowercase__  :   Optional[int]    		=      [-1] * (len(lowercase_   ))
  lowercase__  :   Optional[Any]    		=      0
  lowercase__  :   List[Any]    		=      []
  lowercase__  :   Optional[Any]    		=      [i[:] for i in graph]  # Record original cut, copy.
  while bfs(lowercase_ , lowercase_ , lowercase_ , lowercase_   ):
    lowercase__  :   Optional[int]    		=      float("""Inf"""   )
    lowercase__  :   Tuple    		=      sink

    while s != source:
      # Find the minimum value in select path
      lowercase__  :   List[str]    		=      min(lowercase_ , graph[parent[s]][s]   )
      lowercase__  :   List[str]    		=      parent[s]

    max_flow += path_flow
    lowercase__  :   str    		=      sink

    while v != source:
      lowercase__  :   int    		=      parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      lowercase__  :   Union[str, Any]    		=      parent[v]

  for i in range(len(lowercase_   )   ):
    for j in range(len(graph[0]   )   ):
      if graph[i][j] == 0 and temp[i][j] > 0:
        res.append((i, j)   )

  return res


if __name__ == "__main__":
     print(mincut(test_graph, source=0, sink=5))






