





'''simple docstring'''




class      UpperCamelCase__					:




					"""simple docstring"""


					def __init__(						self				:			str      ,						lowerCamelCase_				:			Any      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			None
							SCREAMING_SNAKE_CASE :				List[Any]		=			None
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			graph

							self._normalize_graph(lowerCamelCase_      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			None


					def lowerCamelCase_			(						self				:			Tuple      ,						lowerCamelCase_				:			int      ,						lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							if sources is int:
									SCREAMING_SNAKE_CASE :				Dict		=			[sources]
							if sinks is int:
									SCREAMING_SNAKE_CASE :				str		=			[sinks]

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

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			sources[0]
							SCREAMING_SNAKE_CASE :				List[str]		=			sinks[0]

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

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

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


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							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				:			Dict      ,						lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			algorithm(self						)







class      UpperCamelCase__					:




					"""simple docstring"""


					def __init__(						self				:			List[str]      ,						lowerCamelCase_				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			flow_network
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			flow_network.verticesCount
							SCREAMING_SNAKE_CASE :				int		=			flow_network.sourceIndex
							SCREAMING_SNAKE_CASE :				Dict		=			flow_network.sinkIndex
							# it's just a reference, so you shouldn't change
							# it in your algorithms, use deep copy before doing that
							SCREAMING_SNAKE_CASE :				List[str]		=			flow_network.graph
							SCREAMING_SNAKE_CASE :				int		=			False


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							if not self.executed:
									self._algorithm()
									SCREAMING_SNAKE_CASE :				Any		=			True



					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							pass







class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""


					def __init__(						self				:			List[Any]      ,						lowerCamelCase_				:			int						):

							'''simple docstring'''



							super().__init__(lowerCamelCase_						)
							# use this to save your result
							SCREAMING_SNAKE_CASE :				int		=			-1



					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							if not self.executed:
									raise Exception("""You should execute algorithm before using its result!"""						)

							return self.maximum_flow







class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""


					def __init__(						self				:			int      ,						lowerCamelCase_				:			Any						):

							'''simple docstring'''



							super().__init__(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Any		=			[[0] * self.verticies_count for i in range(self.verticies_count						)]

							SCREAMING_SNAKE_CASE :				Optional[int]		=			[0] * self.verticies_count
							SCREAMING_SNAKE_CASE :				Tuple		=			[0] * self.verticies_count


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			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
							SCREAMING_SNAKE_CASE :				Any		=			[
							    i
							    for i in range(self.verticies_count						)
							    if i != self.source_index and i != self.sink_index
							]

							# move through list
							SCREAMING_SNAKE_CASE :				Any		=			0
							while i < len(lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			vertices_list[i]
									SCREAMING_SNAKE_CASE :				Tuple		=			self.heights[vertex_index]
									self.process_vertex(lowerCamelCase_						)
									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(lowerCamelCase_						)						)
											SCREAMING_SNAKE_CASE :				List[str]		=			0
									else:
											i += 1

							SCREAMING_SNAKE_CASE :				List[str]		=			sum(self.preflow[self.source_index]						)


					def lowerCamelCase_			(						self				:			str      ,						lowerCamelCase_				:			str						):

							'''simple docstring'''



							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(lowerCamelCase_      ,						lowerCamelCase_						)

									self.relabel(lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			int      ,						lowerCamelCase_				:			str      ,						lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			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				:			Optional[Any]      ,						lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			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):
											SCREAMING_SNAKE_CASE :				Optional[int]		=			self.heights[to_index]

							if min_height is not None:
									SCREAMING_SNAKE_CASE :				Dict		=			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 json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


__UpperCAmelCase            =       {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}




class      UpperCamelCase__					(					nn.Module			):




					"""simple docstring"""


					def __init__(						self				:			Dict      ,						lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



							super().__init__()
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torchvision.models.resnetaaa(pretrained=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			list(model.children()						)[:-2]
							SCREAMING_SNAKE_CASE :				Any		=			nn.Sequential(*lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]						)



					def lowerCamelCase_			(						self				:			List[str]      ,						lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			self.pool(self.model(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				int		=			torch.flatten(lowerCamelCase_      ,						start_dim=2						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			out.transpose(1      ,						2						).contiguous()
							return out  # BxNx2048







class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""


					def __init__(						self				:			str      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			[json.loads(lowerCamelCase_						) for l in open(lowerCamelCase_						)]
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			os.path.dirname(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			tokenizer
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			labels
							SCREAMING_SNAKE_CASE :				str		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			max_seq_length

							SCREAMING_SNAKE_CASE :				List[Any]		=			transforms


					def __len__(						self				:			Any						):

							'''simple docstring'''



							return len(self.data						)


					def __getitem__(						self				:			Optional[int]      ,						lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]      ,						add_special_tokens=lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Tuple		=			sentence[0], sentence[1:-1], sentence[-1]
							SCREAMING_SNAKE_CASE :				int		=			sentence[: self.max_seq_length]

							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.zeros(self.n_classes						)
							SCREAMING_SNAKE_CASE :				List[str]		=			1

							SCREAMING_SNAKE_CASE :				int		=			Image.open(os.path.join(self.data_dir      ,						self.data[index]["""img"""]						)						).convert("""RGB"""						)
							SCREAMING_SNAKE_CASE :				str		=			self.transforms(lowerCamelCase_						)

							return {
							    "image_start_token": start_token,
							    "image_end_token": end_token,
							    "sentence": sentence,
							    "image": image,
							    "label": label,
							}



					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			Counter()
							for row in self.data:
									label_freqs.update(row["""label"""]						)
							return label_freqs







def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Tuple		=			[len(row["""sentence"""]	) for row in batch]
		SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Any		=			len(lowerCamelCase_	), max(lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				int		=			torch.zeros(lowerCamelCase_			,			lowerCamelCase_			,			dtype=torch.long	)
		SCREAMING_SNAKE_CASE :				Any		=			torch.zeros(lowerCamelCase_			,			lowerCamelCase_			,			dtype=torch.long	)

		for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase_			,			lowerCamelCase_	)	):
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			input_row["""sentence"""]
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			1

		SCREAMING_SNAKE_CASE :				List[str]		=			torch.stack([row["""image"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				int		=			torch.stack([row["""label"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				str		=			torch.stack([row["""image_start_token"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				Any		=			torch.stack([row["""image_end_token"""] for row in batch]	)

		return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor



def 		__A		(  ):


		"""simple docstring"""



		return [
		    "Crime",
		    "Drama",
		    "Thriller",
		    "Action",
		    "Comedy",
		    "Romance",
		    "Documentary",
		    "Short",
		    "Mystery",
		    "History",
		    "Family",
		    "Adventure",
		    "Fantasy",
		    "Sci-Fi",
		    "Western",
		    "Horror",
		    "Sport",
		    "War",
		    "Music",
		    "Musical",
		    "Animation",
		    "Biography",
		    "Film-Noir",
		]



def 		__A		(  ):


		"""simple docstring"""



		return transforms.Compose(
		    [
		        transforms.Resize(2_56	),
		        transforms.CenterCrop(2_24	),
		        transforms.ToTensor(),
		        transforms.Normalize(
		            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]			,			std=[0.12_221_994, 0.12_145_835, 0.14_380_469]			,			),
		    ]	)



