




class     lowerCamelCase__:
      def __init__(				self: int    ,       UpperCamelCase_: str    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Tuple   ):
             __lowerCamelCase   =						None
             __lowerCamelCase   =						None
             __lowerCamelCase   =						graph

             self._normalize_graph(_a    ,       _a   )
             __lowerCamelCase   =						len(_a   )
             __lowerCamelCase   =						None
      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: Dict   ):
             if sources is int:
                    __lowerCamelCase   =						[sources]
             if sinks is int:
                    __lowerCamelCase   =						[sinks]

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

             __lowerCamelCase   =						sources[0]
             __lowerCamelCase   =						sinks[0]

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

                    __lowerCamelCase   =						len(self.graph   ) + 1
                    for room in self.graph:
                           room.insert(0    ,       0   )
                    self.graph.insert(0    ,       [0] * size   )
                    for i in sources:
                           __lowerCamelCase   =						max_input_flow
                    __lowerCamelCase   =						0

                    __lowerCamelCase   =						len(self.graph   ) + 1
                    for room in self.graph:
                           room.append(0   )
                    self.graph.append([0] * size   )
                    for i in sinks:
                           __lowerCamelCase   =						max_input_flow
                    __lowerCamelCase   =						size - 1
      def 			lowerCAmelCase__						(				self: int   ):
             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 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: Optional[int]   ):
             __lowerCamelCase   =						algorithm(self   )





class     lowerCamelCase__:
      def __init__(				self: List[Any]    ,       UpperCamelCase_: Optional[int]   ):
             __lowerCamelCase   =						flow_network
             __lowerCamelCase   =						flow_network.verticesCount
             __lowerCamelCase   =						flow_network.sourceIndex
             __lowerCamelCase   =						flow_network.sinkIndex
             # it's just a reference, so you shouldn't change
             # it in your algorithms, use deep copy before doing that
             __lowerCamelCase   =						flow_network.graph
             __lowerCamelCase   =						False
      def 			lowerCAmelCase__						(				self: str   ):
             if not self.executed:
                    self._algorithm()
                    __lowerCamelCase   =						True




      def 			lowerCAmelCase__						(				self: List[str]   ):
             pass





class     lowerCamelCase__(					UpperCAmelCase__):
      def __init__(				self: Any    ,       UpperCamelCase_: Any   ):
             super().__init__(_a   )
             # use this to save your result
             __lowerCamelCase   =						-1




      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             if not self.executed:
                    raise Exception("""You should execute algorithm before using its result!"""   )

             return self.maximum_flow





class     lowerCamelCase__(					UpperCAmelCase__):
      def __init__(				self: Union[str, Any]    ,       UpperCamelCase_: str   ):
             super().__init__(_a   )

             __lowerCamelCase   =						[[0] * self.verticies_count for i in range(self.verticies_count   )]

             __lowerCamelCase   =						[0] * self.verticies_count
             __lowerCamelCase   =						[0] * self.verticies_count
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						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
             __lowerCamelCase   =						[
                 i
                 for i in range(self.verticies_count   )
                 if i != self.source_index and i != self.sink_index
             ]

             # move through list
             __lowerCamelCase   =						0
             while i < len(_a   ):
                    __lowerCamelCase   =						vertices_list[i]
                    __lowerCamelCase   =						self.heights[vertex_index]
                    self.process_vertex(_a   )
                    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(_a   )   )
                           __lowerCamelCase   =						0
                    else:
                           i += 1

             __lowerCamelCase   =						sum(self.preflow[self.source_index]   )
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: int   ):
             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(_a    ,       _a   )

                    self.relabel(_a   )
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: List[Any]   ):
             __lowerCamelCase   =						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 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: Optional[Any]   ):
             __lowerCamelCase   =						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):
                           __lowerCamelCase   =						self.heights[to_index]

             if min_height is not None:
                    __lowerCamelCase   =						min_height + 1


if __name__ == "__main__":
       UpperCAmelCase_									=	[0]
       UpperCAmelCase_									=	[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],
       # ]
       UpperCAmelCase_									=	[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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







"""simple docstring"""




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

from ..utils.generic import ModelOutput






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					dataset
							lowerCamelCase			      =					process
							lowerCamelCase			      =					params





				def __len__(       self ):





							"""simple docstring"""
							return len(self.dataset )





				def __getitem__(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.dataset[i]
							lowerCamelCase			      =					self.process(_a		, **self.params )
							return processed






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





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





							"""simple docstring"""
							lowerCamelCase			      =					loader
							lowerCamelCase			      =					infer
							lowerCamelCase			      =					params
							if loader_batch_size == 1:
										# Let's spare some time by deactivating altogether
										lowerCamelCase			      =					None
							lowerCamelCase			      =					loader_batch_size

							# Internal bookkeeping
							lowerCamelCase			      =					None
							lowerCamelCase			      =					None





				def __len__(       self ):





							"""simple docstring"""
							return len(self.loader )





				def __iter__(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					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
										lowerCamelCase			      =					self._loader_batch_data[self._loader_batch_index]
							else:
										# Batch data is assumed to be BaseModelOutput (or dict)
										lowerCamelCase			      =					{}
										for k, element in self._loader_batch_data.items():
													if isinstance(_a		, _a ):
																# Convert ModelOutput to tuple first
																lowerCamelCase			      =					element.to_tuple()
																if isinstance(element[0]		, torch.Tensor ):
																			lowerCamelCase			      =					tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
																elif isinstance(element[0]		, np.ndarray ):
																			lowerCamelCase			      =					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(_a		, _a ):
																# Those are stored as lists of tensors so need specific unbatching.
																if isinstance(element[0]		, torch.Tensor ):
																			lowerCamelCase			      =					tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
																elif isinstance(element[0]		, np.ndarray ):
																			lowerCamelCase			      =					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
																lowerCamelCase			      =					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

																lowerCamelCase			      =					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
																lowerCamelCase			      =					np.expand_dims(element[self._loader_batch_index]		, 0 )
													else:
																# This is typically a list, so no need to `unsqueeze`.
																lowerCamelCase			      =					element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
										lowerCamelCase			      =					self._loader_batch_data.__class__(_a )
							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
							lowerCamelCase			      =					next(self.iterator )
							lowerCamelCase			      =					self.infer(_a		, **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(_a		, torch.Tensor ):
													lowerCamelCase			      =					processed
										else:
													lowerCamelCase			      =					list(processed.keys() )[0]
													lowerCamelCase			      =					processed[key]
										if isinstance(_a		, _a ):
													lowerCamelCase			      =					len(_a )
										else:
													lowerCamelCase			      =					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.
													lowerCamelCase			      =					observed_batch_size
										# Setting internal index to unwrap the batch
										lowerCamelCase			      =					processed
										lowerCamelCase			      =					0
										return self.loader_batch_item()
							else:
										# We're not unrolling batches
										return processed






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





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





							"""simple docstring"""
							super().__init__(_a		, _a		, _a )





				def __iter__(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					iter(self.loader )
							lowerCamelCase			      =					None
							return self





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							if self.subiterator is None:
										lowerCamelCase			      =					self.infer(next(self.iterator )		, **self.params )
							try:
										# Try to return next item
										lowerCamelCase			      =					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
										lowerCamelCase			      =					self.infer(next(self.iterator )		, **self.params )
										lowerCamelCase			      =					next(self.subiterator )
							return processed






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __iter__(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					iter(self.loader )
							return self





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							# Extremely similar to PipelineIterator in its unpacking mechanism
							# BUT, we have an extra required item which is the presence of `is_last`
							# That is because everything is flattened by `PipelineChunkIterator` we
							# need to keep track of how to regroup here in the original `process`
							# boundaries so that `process` and `postprocess` see the same data.

							# This iterator accumulates items (possibly while unbatching) until it
							# its a `is_last` and then just passes it on to the caller.
							lowerCamelCase			      =					False
							lowerCamelCase			      =					[]
							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:
													lowerCamelCase			      =					self.loader_batch_item()
													lowerCamelCase			      =					item.pop("""is_last""" )
													accumulator.append(_a )
													if is_last:
																return accumulator

							while not is_last:
										lowerCamelCase			      =					self.infer(next(self.iterator )		, **self.params )
										if self.loader_batch_size is not None:
													if isinstance(_a		, torch.Tensor ):
																lowerCamelCase			      =					processed
													else:
																lowerCamelCase			      =					list(processed.keys() )[0]
																lowerCamelCase			      =					processed[key]
													if isinstance(_a		, _a ):
																lowerCamelCase			      =					len(_a )
													else:
																lowerCamelCase			      =					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.
																lowerCamelCase			      =					observed_batch_size
													lowerCamelCase			      =					processed
													lowerCamelCase			      =					0
													while self._loader_batch_index < self.loader_batch_size:
																lowerCamelCase			      =					self.loader_batch_item()
																lowerCamelCase			      =					item.pop("""is_last""" )
																accumulator.append(_a )
																if is_last:
																			return accumulator
										else:
													lowerCamelCase			      =					processed
													lowerCamelCase			      =					item.pop("""is_last""" )
													accumulator.append(_a )
							return accumulator






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					dataset
							lowerCamelCase			      =					key





				def __len__(       self ):





							"""simple docstring"""
							return len(self.dataset )





				def __getitem__(       self		, _a ):





							"""simple docstring"""
							return self.dataset[i][self.key]






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					dataset
							lowerCamelCase			      =					keya
							lowerCamelCase			      =					keya





				def __len__(       self ):





							"""simple docstring"""
							return len(self.dataset )





				def __getitem__(       self		, _a ):





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



