


"""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


a_       							=    {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 snake_case      (  nn.Module):







		def __init__(					self      :       Union[str, Any]    ,   a__      :       Dict		)       ->						Any:





						'''simple docstring'''







						super().__init__()
						_A					  =			torchvision.models.resnetaaa(pretrained=a__		)
						_A					  =			list(model.children()		)[:-2]
						_A					  =			nn.Sequential(*a__		)
						_A					  =			nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]		)







		def  a_ (					self      :       List[str]    ,   a__      :       str		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			self.pool(self.model(a__		)		)
						_A					  =			torch.flatten(a__    ,   start_dim=2		)
						_A					  =			out.transpose(1    ,   2		).contiguous()
						return out  # BxNx2048





class snake_case      (  UpperCAmelCase__):







		def __init__(					self      :       str    ,   a__      :       Dict    ,   a__      :       Optional[Any]    ,   a__      :       Tuple    ,   a__      :       Any    ,   a__      :       List[Any]		)       ->						Any:





						'''simple docstring'''







						_A					  =			[json.loads(a__		) for l in open(a__		)]
						_A					  =			os.path.dirname(a__		)
						_A					  =			tokenizer
						_A					  =			labels
						_A					  =			len(a__		)
						_A					  =			max_seq_length

						_A					  =			transforms







		def __len__(					self      :       Union[str, Any]		)       ->						int:





						'''simple docstring'''







						return len(self.data		)







		def __getitem__(					self      :       int    ,   a__      :       Union[str, Any]		)       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]    ,   add_special_tokens=a__		)		)
						_A					  =			sentence[0], sentence[1:-1], sentence[-1]
						_A					  =			sentence[: self.max_seq_length]

						_A					  =			torch.zeros(self.n_classes		)
						_A					  =			1

						_A					  =			Image.open(os.path.join(self.data_dir    ,   self.data[index]["img"]		)		).convert("RGB"		)
						_A					  =			self.transforms(a__		)

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







		def  a_ (					self      :       Any		)       ->						List[Any]:





						'''simple docstring'''







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






def      a__ (				__lowercase   )	->	List[str]:
				_A					  =			[len(row["sentence"]   ) for row in batch]
				_A					  =			len(__lowercase   ), max(__lowercase   )

				_A					  =			torch.zeros(__lowercase     ,			__lowercase     ,			dtype=torch.long   )
				_A					  =			torch.zeros(__lowercase     ,			__lowercase     ,			dtype=torch.long   )

				for i_batch, (input_row, length) in enumerate(zip(__lowercase     ,			__lowercase   )   ):
								_A					  =			input_row["""sentence"""]
								_A					  =			1

				_A					  =			torch.stack([row["image"] for row in batch]   )
				_A					  =			torch.stack([row["label"] for row in batch]   )
				_A					  =			torch.stack([row["image_start_token"] for row in batch]   )
				_A					  =			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__ (				)	->	Any:
				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__ (				)	->	Dict:
				return transforms.Compose(
				    [
				        transforms.Resize(256   ),
				        transforms.CenterCrop(224   ),
				        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]     ,			),
				    ]   )



"""simple docstring"""


class snake_case      :







		def __init__(					self      :       Optional[int]    ,   a__      :       List[Any]    ,   a__      :       List[str]    ,   a__      :       Tuple		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			None
						_A					  =			None
						_A					  =			graph

						self._normalize_graph(a__    ,   a__		)
						_A					  =			len(a__		)
						_A					  =			None







		def  a_ (					self      :       str    ,   a__      :       List[str]    ,   a__      :       List[Any]		)       ->						Dict:





						'''simple docstring'''







						if sources is int:
										_A					  =			[sources]
						if sinks is int:
										_A					  =			[sinks]

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

						_A					  =			sources[0]
						_A					  =			sinks[0]

						# make fake vertex if there are more
						# than one source or sink
						if len(a__		) > 1 or len(a__		) > 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  a_ (					self      :       Optional[int]		)       ->						List[Any]:





						'''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  a_ (					self      :       List[Any]    ,   a__      :       Optional[Any]		)       ->						str:





						'''simple docstring'''







						_A					  =			algorithm(self		)





class snake_case      :







		def __init__(					self      :       List[str]    ,   a__      :       List[str]		)       ->						Union[str, Any]:





						'''simple docstring'''







						_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  a_ (					self      :       Optional[Any]		)       ->						List[Any]:





						'''simple docstring'''







						if not self.executed:
										self._algorithm()
										_A					  =			True







		def  a_ (					self      :       Any		)       ->						int:





						'''simple docstring'''







						pass





class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       Optional[Any]    ,   a__      :       Dict		)       ->						List[str]:





						'''simple docstring'''







						super().__init__(a__		)
						# use this to save your result
						_A					  =			-1







		def  a_ (					self      :       Any		)       ->						List[str]:





						'''simple docstring'''







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

						return self.maximum_flow





class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       Union[str, Any]    ,   a__      :       Union[str, Any]		)       ->						Dict:





						'''simple docstring'''







						super().__init__(a__		)

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

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







		def  a_ (					self      :       Any		)       ->						Dict:





						'''simple docstring'''







						_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(a__		):
										_A					  =			vertices_list[i]
										_A					  =			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__		)		)
														_A					  =			0
										else:
														i += 1

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







		def  a_ (					self      :       Dict    ,   a__      :       Any		)       ->						Optional[int]:





						'''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(a__    ,   a__		)

										self.relabel(a__		)







		def  a_ (					self      :       str    ,   a__      :       Optional[int]    ,   a__      :       List[Any]		)       ->						Optional[int]:





						'''simple docstring'''







						_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  a_ (					self      :       Any    ,   a__      :       Dict		)       ->						Any:





						'''simple docstring'''







						_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__":
		a_       							=    [0]
		a_       							=    [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],
		# ]
		a_       							=    [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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