



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


lowerCamelCase__			= {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					):





							def __init__(   self		:		Any      ,				__lowercase		:		Optional[int]	):

											'''simple docstring'''
											super().__init__()
											__a       =		torchvision.models.resnetaaa(pretrained=__lowercase	)
											__a       =		list(model.children()	)[:-2]
											__a       =		nn.Sequential(*__lowercase	)
											__a       =		nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)







							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		Union[str, Any]	):

											'''simple docstring'''
											# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
											__a       =		self.pool(self.model(__lowercase	)	)
											__a       =		torch.flatten(__lowercase      ,				start_dim=2	)
											__a       =		out.transpose(1      ,				2	).contiguous()
											return out  # BxNx2048






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):





							def __init__(   self		:		Optional[Any]      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		int      ,				__lowercase		:		Any      ,				__lowercase		:		Tuple	):

											'''simple docstring'''
											__a       =		[json.loads(__lowercase	) for l in open(__lowercase	)]
											__a       =		os.path.dirname(__lowercase	)
											__a       =		tokenizer
											__a       =		labels
											__a       =		len(__lowercase	)
											__a       =		max_seq_length

											__a       =		transforms





							def __len__(   self		:		List[Any]	):

											'''simple docstring'''
											return len(self.data	)





							def __getitem__(   self		:		List[str]      ,				__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]      ,				add_special_tokens=__lowercase	)	)
											__a     ,					__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(__lowercase	)

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







							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a       =		Counter()
											for row in self.data:
															label_freqs.update(row["""label"""]	)
											return label_freqs



def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Any    ):
				"""simple docstring"""







				__a       =		[len(row["""sentence"""]    ) for row in batch]
				__a     ,					__a       =		len(_SCREAMING_SNAKE_CASE    ), max(_SCREAMING_SNAKE_CASE    )

				__a       =		torch.zeros(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     dtype=torch.long    )
				__a       =		torch.zeros(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     dtype=torch.long    )

				for i_batch, (input_row, length) in enumerate(zip(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )    ):
								__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   lowerCAmelCase__						(							):
				"""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   lowerCAmelCase__						(							):
				"""simple docstring"""







				return transforms.Compose(
				    [
				        transforms.Resize(256    ),
				        transforms.CenterCrop(224    ),
				        transforms.ToTensor(),
				        transforms.Normalize(
				            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]				,     std=[0.1222_1994, 0.1214_5835, 0.1438_0469]				,     ),
				    ]    )





from __future__ import annotations

from collections import Counter
from random import random






class    SCREAMING_SNAKE_CASE			:





							def __init__(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		str      ,				__lowercase		:		str      ,				__lowercase		:		float	):

											'''simple docstring'''
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											__a       =		probability





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											return list(self.connections	)







							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		0
											__a       =		random()

											for dest in self.connections[node]:
															current_probability += self.connections[node][dest]
															if current_probability > random_value:
																			return dest
											return ""



def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			list[tuple[str, str, float]]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				__a       =		Counter(graph.get_nodes()    )
				__a       =		start

				for _ in range(_SCREAMING_SNAKE_CASE    ):
								__a       =		graph.transition(_SCREAMING_SNAKE_CASE    )
								visited[node] += 1

				return visited


if __name__ == "__main__":
						import doctest

						doctest.testmod()
