
'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	int      =							dataset
       a_      :	int      =							process
       a_      :	Dict      =							params


     def __len__(      self ):

       '''simple docstring'''

       return len(self.dataset )






     def __getitem__(      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	List[str]      =							self.dataset[i]
       a_      :	str      =							self.process(lowerCAmelCase_							,		**self.params )
       return processed







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       a_      :	int      =							loader
       a_      :	Any      =							infer
       a_      :	Union[str, Any]      =							params
       if loader_batch_size == 1:
         # Let's spare some time by deactivating altogether
         a_      :	Any      =							None
       a_      :	Optional[Any]      =							loader_batch_size

       # Internal bookkeeping
       a_      :	List[str]      =							None
       a_      :	Dict      =							None


     def __len__(      self ):

       '''simple docstring'''

       return len(self.loader )


     def __iter__(      self ):

       '''simple docstring'''

       a_      :	List[str]      =							iter(self.loader )
       return self


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       if isinstance(self._loader_batch_data							,		torch.Tensor ):
         # Batch data is simple tensor, just fetch the slice
         a_      :	str      =							self._loader_batch_data[self._loader_batch_index]
       else:
         # Batch data is assumed to be BaseModelOutput (or dict)
         a_      :	Dict      =							{}
         for k, element in self._loader_batch_data.items():
           if isinstance(lowerCAmelCase_							,		lowerCAmelCase_ ):
             # Convert ModelOutput to tuple first
             a_      :	str      =							element.to_tuple()
             if isinstance(element[0]							,		torch.Tensor ):
               a_      :	int      =							tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
             elif isinstance(element[0]							,		np.ndarray ):
               a_      :	List[Any]      =							tuple(np.expand_dims(el[self._loader_batch_index]							,		0 ) for el in element )
             continue
           if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(lowerCAmelCase_							,		lowerCAmelCase_ ):
             # Those are stored as lists of tensors so need specific unbatching.
             if isinstance(element[0]							,		torch.Tensor ):
               a_      :	List[str]      =							tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
             elif isinstance(element[0]							,		np.ndarray ):
               a_      :	Union[str, Any]      =							tuple(np.expand_dims(el[self._loader_batch_index]							,		0 ) for el in element )
             continue
           if element is None:
             # This can happen for optional data that get passed around
             a_      :	Optional[Any]      =							None
           elif isinstance(element[self._loader_batch_index]							,		torch.Tensor ):
             # Take correct batch data, but make it looked like batch_size=1
             # For compatibility with other methods within transformers

             a_      :	Dict      =							element[self._loader_batch_index].unsqueeze(0 )
           elif isinstance(element[self._loader_batch_index]							,		np.ndarray ):
             # Take correct batch data, but make it looked like batch_size=1
             # For compatibility with other methods within transformers
             a_      :	Optional[int]      =							np.expand_dims(element[self._loader_batch_index]							,		0 )
           else:
             # This is typically a list, so no need to `unsqueeze`.
             a_      :	Dict      =							element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
         a_      :	Union[str, Any]      =							self._loader_batch_data.__class__(lowerCAmelCase_ )
       self._loader_batch_index += 1
       return result






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
         # We are currently unrolling a batch so we just need to return
         # the current item within a batch
         return self.loader_batch_item()

       # We're out of items within a batch
       a_      :	str      =							next(self.iterator )
       a_      :	List[Any]      =							self.infer(lowerCAmelCase_							,		**self.params )
       # We now have a batch of "inferred things".
       if self.loader_batch_size is not None:
         # Try to infer the size of the batch
         if isinstance(lowerCAmelCase_							,		torch.Tensor ):
           a_      :	List[Any]      =							processed
         else:
           a_      :	int      =							list(processed.keys() )[0]
           a_      :	Optional[int]      =							processed[key]
         if isinstance(lowerCAmelCase_							,		lowerCAmelCase_ ):
           a_      :	str      =							len(lowerCAmelCase_ )
         else:
           a_      :	Union[str, Any]      =							first_tensor.shape[0]
         if 0 < observed_batch_size < self.loader_batch_size:
           # could be last batch so we can't unroll as many
           # elements.
           a_      :	Dict      =							observed_batch_size
         # Setting internal index to unwrap the batch
         a_      :	List[Any]      =							processed
         a_      :	Any      =							0
         return self.loader_batch_item()
       else:
         # We're not unrolling batches
         return processed







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       super().__init__(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )


     def __iter__(      self ):

       '''simple docstring'''

       a_      :	Any      =							iter(self.loader )
       a_      :	Dict      =							None
       return self






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       if self.subiterator is None:
         a_      :	Union[str, Any]      =							self.infer(next(self.iterator )							,		**self.params )
       try:
         # Try to return next item
         a_      :	Optional[int]      =							next(self.subiterator )
       except StopIteration:
         # When a preprocess iterator ends, we can start lookig at the next item
         # ChunkIterator will keep feeding until ALL elements of iterator
         # all have created their subiterator and have been iterating against.
         #
         # Another way to look at it, is we're basically flattening lists of lists
         # into a single list, but with generators
         a_      :	Union[str, Any]      =							self.infer(next(self.iterator )							,		**self.params )
         a_      :	int      =							next(self.subiterator )
       return processed







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     def __iter__(      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							iter(self.loader )
       return self






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							False
       a_      :	Any      =							[]
       if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
         while self._loader_batch_index < self.loader_batch_size:
           a_      :	Optional[Any]      =							self.loader_batch_item()
           a_      :	Optional[int]      =							item.pop("""is_last""" )
           accumulator.append(lowerCAmelCase_ )
           if is_last:
             return accumulator

       while not is_last:
         a_      :	str      =							self.infer(next(self.iterator )							,		**self.params )
         if self.loader_batch_size is not None:
           if isinstance(lowerCAmelCase_							,		torch.Tensor ):
             a_      :	Optional[int]      =							processed
           else:
             a_      :	int      =							list(processed.keys() )[0]
             a_      :	Optional[Any]      =							processed[key]
           if isinstance(lowerCAmelCase_							,		lowerCAmelCase_ ):
             a_      :	Optional[Any]      =							len(lowerCAmelCase_ )
           else:
             a_      :	Optional[int]      =							first_tensor.shape[0]
           if 0 < observed_batch_size < self.loader_batch_size:
             # could be last batch so we can't unroll as many
             # elements.
             a_      :	List[Any]      =							observed_batch_size
           a_      :	Any      =							processed
           a_      :	int      =							0
           while self._loader_batch_index < self.loader_batch_size:
             a_      :	Optional[int]      =							self.loader_batch_item()
             a_      :	Dict      =							item.pop("""is_last""" )
             accumulator.append(lowerCAmelCase_ )
             if is_last:
               return accumulator
         else:
           a_      :	List[Any]      =							processed
           a_      :	Dict      =							item.pop("""is_last""" )
           accumulator.append(lowerCAmelCase_ )
       return accumulator







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	List[str]      =							dataset
       a_      :	List[str]      =							key


     def __len__(      self ):

       '''simple docstring'''

       return len(self.dataset )






     def __getitem__(      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return self.dataset[i][self.key]







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	List[str]      =							dataset
       a_      :	List[str]      =							keya
       a_      :	Union[str, Any]      =							keya


     def __len__(      self ):

       '''simple docstring'''

       return len(self.dataset )






     def __getitem__(      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}




'''simple docstring'''

def 	_snake_case						(      A_       :    int     ,		A_       :    Optional[Any]     ,		A_       :    Optional[int]     ,		A_       :    Dict   ):
  """simple docstring"""







  a_      :	Optional[int]      =							[False] * len(A_   )
  a_      :	List[str]      =							[]
  queue.append(A_   )
  a_      :	Union[str, Any]      =							True

  while queue:
    a_      :	List[Any]      =							queue.pop(0   )
    for ind in range(len(graph[u]   )   ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(A_   )
        a_      :	Any      =							True
        a_      :	List[Any]      =							u

  return visited[t]






def 	_snake_case						(      A_       :    Union[str, Any]     ,		A_       :    Union[str, Any]     ,		A_       :    List[Any]   ):
  """simple docstring"""







  a_      :	Dict      =							[-1] * (len(A_   ))
  a_      :	List[str]      =							0
  while bfs(A_     ,		A_     ,		A_     ,		A_   ):
    a_      :	Any      =							float("""Inf"""   )
    a_      :	Any      =							sink

    while s != source:
      # Find the minimum value in select path
      a_      :	Any      =							min(A_     ,		graph[parent[s]][s]   )
      a_      :	List[Any]      =							parent[s]

    max_flow += path_flow
    a_      :	Tuple      =							sink

    while v != source:
      a_      :	Tuple      =							parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      a_      :	List[Any]      =							parent[v]
  return max_flow


__snake_case:  List[str]								=			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

__snake_case							,__snake_case:  int								=			0, 5




print(ford_fulkerson(graph, source, sink))


