






'''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


__lowerCamelCase   :					Union[str, Any]   							=					{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    A_	(nn.Module							):
							"""simple docstring"""




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


										'''simple docstring'''




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






							def 							_A	(		self			:List[Any]       ,							lowerCAmelCase__			:Any				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					str   =       self.pool(self.model(lowerCAmelCase__				)				)
										snake_case_				:					Union[str, Any]   =       torch.flatten(lowerCAmelCase__       ,							start_dim=2				)
										snake_case_				:					Union[str, Any]   =       out.transpose(1       ,							2				).contiguous()
										return out  # BxNx2048







class    A_	(a_							):
							"""simple docstring"""




							def __init__(		self			:str       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:Optional[int]       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:Optional[int]       ,							lowerCAmelCase__			:str				)				->  int:


										'''simple docstring'''




										snake_case_				:					str   =       [json.loads(lowerCAmelCase__				) for l in open(lowerCAmelCase__				)]
										snake_case_				:					Tuple   =       os.path.dirname(lowerCAmelCase__				)
										snake_case_				:					Any   =       tokenizer
										snake_case_				:					List[Any]   =       labels
										snake_case_				:					Union[str, Any]   =       len(lowerCAmelCase__				)
										snake_case_				:					str   =       max_seq_length

										snake_case_				:					Tuple   =       transforms

							def __len__(		self			:Dict				)				->  Any:


										'''simple docstring'''




										return len(self.data				)

							def __getitem__(		self			:Any       ,							lowerCAmelCase__			:int				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]       ,							add_special_tokens=lowerCAmelCase__				)				)
										snake_case_				:					str   =       sentence[0], sentence[1:-1], sentence[-1]
										snake_case_				:					Tuple   =       sentence[: self.max_seq_length]

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

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

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






							def 							_A	(		self			:str				)				->  Any:


										'''simple docstring'''




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

def 	__UpperCAmelCase       ( __magic_name__						)->    Tuple:



			"""simple docstring"""



			snake_case_				:					Optional[int]   =       [len(row["sentence"]						) for row in batch]
			snake_case_				:					Optional[Any]   =       len(__magic_name__						), max(__magic_name__						)

			snake_case_				:					Any   =       torch.zeros(__magic_name__     ,__magic_name__     ,dtype=torch.long						)
			snake_case_				:					List[Any]   =       torch.zeros(__magic_name__     ,__magic_name__     ,dtype=torch.long						)

			for i_batch, (input_row, length) in enumerate(zip(__magic_name__     ,__magic_name__						)						):
						snake_case_				:					Optional[int]   =       input_row["sentence"]
						snake_case_				:					List[str]   =       1

			snake_case_				:					Dict   =       torch.stack([row["image"] for row in batch]						)
			snake_case_				:					Dict   =       torch.stack([row["label"] for row in batch]						)
			snake_case_				:					int   =       torch.stack([row["image_start_token"] for row in batch]						)
			snake_case_				:					List[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 	__UpperCAmelCase       ( )->    List[str]:



			"""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       ( )->    str:



			"""simple docstring"""



			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'''




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



			return math.sqrt(sum(pow(a - b     ,2						) for a, b in zip(__magic_name__     ,__magic_name__						)						)						)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    list[list[list[float] | float]]:



			"""simple docstring"""



			if dataset.ndim != value_array.ndim:
						snake_case_				:					int   =       (
						    "Wrong input data's dimensions... "
						    F'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
						)
						raise ValueError(__magic_name__						)

			try:
						if dataset.shape[1] != value_array.shape[1]:
									snake_case_				:					Dict   =       (
									    "Wrong input data's shape... "
									    F'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
									)
									raise ValueError(__magic_name__						)
			except IndexError:
						if dataset.ndim != value_array.ndim:
									raise TypeError("Wrong shape"						)

			if dataset.dtype != value_array.dtype:
						snake_case_				:					Dict   =       (
						    "Input data have different datatype... "
						    F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
						)
						raise TypeError(__magic_name__						)

			snake_case_				:					Optional[int]   =       []

			for value in value_array:
						snake_case_				:					List[str]   =       euclidean(__magic_name__     ,dataset[0]						)
						snake_case_				:					int   =       dataset[0].tolist()

						for dataset_value in dataset[1:]:
									snake_case_				:					Optional[Any]   =       euclidean(__magic_name__     ,__magic_name__						)

									if dist > temp_dist:
												snake_case_				:					Tuple   =       temp_dist
												snake_case_				:					Optional[int]   =       dataset_value.tolist()

						answer.append([vector, dist]						)

			return answer

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



			return np.dot(__magic_name__     ,__magic_name__						) / (norm(__magic_name__						) * norm(__magic_name__						))


if __name__ == "__main__":
		import doctest

		doctest.testmod()




