'''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


lowercase_     		= {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   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A				) ->     Tuple:
								super().__init__()
								_lowerCAmelCase										=torchvision.models.resnetaaa(pretrained=__A				)
								_lowerCAmelCase										=list(model.children()				)[:-2]
								_lowerCAmelCase										=nn.Sequential(*__A				)
								_lowerCAmelCase										=nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     List[Any]:
								# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
								_lowerCAmelCase										=self.pool(self.model(__A				)				)
								_lowerCAmelCase										=torch.flatten(__A		,						start_dim=2				)
								_lowerCAmelCase										=out.transpose(1		,						2				).contiguous()
								return out  # BxNx2048

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     Optional[Any]:
								_lowerCAmelCase										=[json.loads(__A				) for l in open(__A				)]
								_lowerCAmelCase										=os.path.dirname(__A				)
								_lowerCAmelCase										=tokenizer
								_lowerCAmelCase										=labels
								_lowerCAmelCase										=len(__A				)
								_lowerCAmelCase										=max_seq_length

								_lowerCAmelCase										=transforms






		def __len__(   self				) ->     Tuple:
								return len(self.data				)






		def __getitem__(   self		,						__A				) ->     Dict:
								_lowerCAmelCase										=torch.LongTensor(self.tokenizer.encode(self.data[index]['text']		,						add_special_tokens=__A				)				)
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=sentence[0], sentence[1:-1], sentence[-1]
								_lowerCAmelCase										=sentence[: self.max_seq_length]

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

								_lowerCAmelCase										=Image.open(os.path.join(self.data_dir		,						self.data[index]['img']				)				).convert('RGB'				)
								_lowerCAmelCase										=self.transforms(__A				)

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






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								_lowerCAmelCase										=Counter()
								for row in self.data:
														label_freqs.update(row['label']				)
								return label_freqs



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=[len(row['sentence']      ) for row in batch]
						_lowerCAmelCase						,				_lowerCAmelCase										=len(a__      ), max(a__      )

						_lowerCAmelCase										=torch.zeros(a__			,      a__			,      dtype=torch.long      )
						_lowerCAmelCase										=torch.zeros(a__			,      a__			,      dtype=torch.long      )

						for i_batch, (input_row, length) in enumerate(zip(a__			,      a__      )      ):
												_lowerCAmelCase										=input_row['sentence']
												_lowerCAmelCase										=1

						_lowerCAmelCase										=torch.stack([row['image'] for row in batch]      )
						_lowerCAmelCase										=torch.stack([row['label'] for row in batch]      )
						_lowerCAmelCase										=torch.stack([row['image_start_token'] for row in batch]      )
						_lowerCAmelCase										=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 							UpperCamelCase__			(			):


						'''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 							UpperCamelCase__			(			):


						'''simple docstring'''






						return transforms.Compose(
						    [
						        transforms.Resize(2_5_6      ),
						        transforms.CenterCrop(2_2_4      ),
						        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'''

from __future__ import annotations

from typing import Any
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A				) ->     None:
								_lowerCAmelCase										=num_of_nodes
								_lowerCAmelCase										=[]
								_lowerCAmelCase										={}






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     None:
								self.m_edges.append([u_node, v_node, weight]				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     int:

								if self.m_component[u_node] == u_node:
														return u_node
								return self.find_component(self.m_component[u_node]				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     None:

								if self.m_component[u_node] != u_node:
														for k in self.m_component:
																				_lowerCAmelCase										=self.find_component(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     None:

								if component_size[u_node] <= component_size[v_node]:
														_lowerCAmelCase										=v_node
														component_size[v_node] += component_size[u_node]
														self.set_component(__A				)

								elif component_size[u_node] >= component_size[v_node]:
														_lowerCAmelCase										=self.find_component(__A				)
														component_size[u_node] += component_size[v_node]
														self.set_component(__A				)






		def 		UpperCamelCase__       (   self				) ->     None:
								_lowerCAmelCase										=[]
								_lowerCAmelCase										=0

								_lowerCAmelCase										=[-1] * self.m_num_of_nodes

								# A list of components (initialized to all of the nodes)
								for node in range(self.m_num_of_nodes				):
														self.m_component.update({node: node}				)
														component_size.append(1				)

								_lowerCAmelCase										=self.m_num_of_nodes

								while num_of_components > 1:
														for edge in self.m_edges:
																				_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=edge

																				_lowerCAmelCase										=self.m_component[u]
																				_lowerCAmelCase										=self.m_component[v]

																				if u_component != v_component:

																										for component in (u_component, v_component):
																																if (
																																    minimum_weight_edge[component] == -1
																																    or minimum_weight_edge[component][2] > w
																																):
																																						_lowerCAmelCase										=[u, v, w]

														for edge in minimum_weight_edge:
																				if isinstance(__A		,						__A				):
																										_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=edge

																										_lowerCAmelCase										=self.m_component[u]
																										_lowerCAmelCase										=self.m_component[v]

																										if u_component != v_component:
																																mst_weight += w
																																self.union(__A		,						__A		,						__A				)
																																print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''				)
																																num_of_components -= 1

														_lowerCAmelCase										=[-1] * self.m_num_of_nodes
								print(F'''The total weight of the minimal spanning tree is: {mst_weight}'''				)



def 							UpperCamelCase__			(			):


						'''simple docstring'''








if __name__ == "__main__":
							import doctest

							doctest.testmod()





