





"""simple docstring"""




class     UpperCamelCase__:





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Union[str, Any]:
													A__	     =							None
													A__	     =							None
													A__	     =							graph

													self._normalize_graph(__UpperCAmelCase		,__UpperCAmelCase     )
													A__	     =							len(__UpperCAmelCase     )
													A__	     =							None





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				List[Any]:
													if sources is int:
																				A__	     =							[sources]
													if sinks is int:
																				A__	     =							[sinks]

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

													A__	     =							sources[0]
													A__	     =							sinks[0]

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

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

																				A__	     =							len(self.graph     ) + 1
																				for room in self.graph:
																											room.append(0     )
																				self.graph.append([0] * size     )
																				for i in sinks:
																											A__	     =							max_input_flow
																				A__	     =							size - 1





						def 				snake_case__	(       self     )					->				List[Any]:
													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 				snake_case__	(       self		,__UpperCAmelCase     )					->				Optional[Any]:
													A__	     =							algorithm(self     )






class     UpperCamelCase__:





						def __init__(       self		,__UpperCAmelCase     )					->				Optional[Any]:
													A__	     =							flow_network
													A__	     =							flow_network.verticesCount
													A__	     =							flow_network.sourceIndex
													A__	     =							flow_network.sinkIndex
													# it's just a reference, so you shouldn't change
													# it in your algorithms, use deep copy before doing that
													A__	     =							flow_network.graph
													A__	     =							False





						def 				snake_case__	(       self     )					->				List[str]:
													if not self.executed:
																				self._algorithm()
																				A__	     =							True





						def 				snake_case__	(       self     )					->				int:
													pass






class     UpperCamelCase__(			__A ):





						def __init__(       self		,__UpperCAmelCase     )					->				Dict:
													super().__init__(__UpperCAmelCase     )
													# use this to save your result
													A__	     =							-1





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

													return self.maximum_flow






class     UpperCamelCase__(			__A ):





						def __init__(       self		,__UpperCAmelCase     )					->				int:
													super().__init__(__UpperCAmelCase     )

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

													A__	     =							[0] * self.verticies_count
													A__	     =							[0] * self.verticies_count





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							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
													A__	     =							[
													    i
													    for i in range(self.verticies_count     )
													    if i != self.source_index and i != self.sink_index
													]

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

													A__	     =							sum(self.preflow[self.source_index]     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Tuple:
													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(__UpperCAmelCase		,__UpperCAmelCase     )

																				self.relabel(__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Optional[int]:
													A__	     =							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 				snake_case__	(       self		,__UpperCAmelCase     )					->				Union[str, Any]:
													A__	     =							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):
																											A__	     =							self.heights[to_index]

													if min_height is not None:
																				A__	     =							min_height + 1


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

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

				print(F'''maximum flow is {maximum_flow}''')






"""simple docstring"""




from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
				import numpy as np
				from decord import VideoReader


if is_torch_available():
				from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

__lowerCamelCase											=  logging.get_logger(__name__)
@add_end_docstrings(__A )
class     UpperCamelCase__(			__A ):





						def __init__(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[Any]:
													super().__init__(*__UpperCAmelCase		,**__UpperCAmelCase     )
													requires_backends(self		,'decord'     )
													self.check_model_type(__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None     )					->				Any:
													A__	     =							{}
													if frame_sampling_rate is not None:
																				A__	     =							frame_sampling_rate
													if num_frames is not None:
																				A__	     =							num_frames

													A__	     =							{}
													if top_k is not None:
																				A__	     =							top_k
													return preprocess_params, {}, postprocess_params





						def __call__(       self		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[int]:
													return super().__call__(__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=None		,__UpperCAmelCase=1     )					->				List[Any]:
													if num_frames is None:
																				A__	     =							self.model.config.num_frames

													if video.startswith('http://'     ) or video.startswith('https://'     ):
																				A__	     =							BytesIO(requests.get(__UpperCAmelCase     ).content     )

													A__	     =							VideoReader(__UpperCAmelCase     )
													videoreader.seek(0     )

													A__	     =							0
													A__	     =							num_frames * frame_sampling_rate - 1
													A__	     =							np.linspace(__UpperCAmelCase		,__UpperCAmelCase		,num=__UpperCAmelCase		,dtype=np.intaa     )

													A__	     =							videoreader.get_batch(__UpperCAmelCase     ).asnumpy()
													A__	     =							list(__UpperCAmelCase     )

													A__	     =							self.image_processor(__UpperCAmelCase		,return_tensors=self.framework     )
													return model_inputs





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Optional[int]:
													A__	     =							self.model(**__UpperCAmelCase     )
													return model_outputs





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=5     )					->				int:
													if top_k > self.model.config.num_labels:
																				A__	     =							self.model.config.num_labels

													if self.framework == "pt":
																				A__	     =							model_outputs.logits.softmax(-1     )[0]
																				A__     ,  A__	     =							probs.topk(__UpperCAmelCase     )
													else:
																				raise ValueError(f'''Unsupported framework: {self.framework}'''     )

													A__	     =							scores.tolist()
													A__	     =							ids.tolist()
													return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__UpperCAmelCase		,__UpperCAmelCase     )]





