
"""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     _UpperCAmelCase(    nn.Module	):



		def __init__(					self				,		__a)       ->       Optional[int]:






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




		def 						UpperCAmelCase							(					self				,		__a)       ->       List[Any]:






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






class     _UpperCAmelCase(    lowerCamelCase	):



		def __init__(					self				,		__a				,		__a				,		__a				,		__a				,		__a)       ->       Any:






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

							_UpperCamelCase							 =  transforms



		def __len__(					self)       ->       Optional[int]:






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



		def __getitem__(					self				,		__a)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase							 =  torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				,		add_special_tokens=__a))
							_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  sentence[0], sentence[1:-1], sentence[-1]
							_UpperCamelCase							 =  sentence[: self.max_seq_length]

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

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

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




		def 						UpperCAmelCase							(					self)       ->       Any:






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





def       lowerCamelCase__		(			__snake_case	)							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase							 =  [len(row['''sentence''']	) for row in batch]
					_UpperCamelCase ,  _UpperCamelCase							 =  len(__snake_case	), max(__snake_case	)

					_UpperCamelCase							 =  torch.zeros(__snake_case,     __snake_case,     dtype=torch.long	)
					_UpperCamelCase							 =  torch.zeros(__snake_case,     __snake_case,     dtype=torch.long	)

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

					_UpperCamelCase							 =  torch.stack([row['''image'''] for row in batch]	)
					_UpperCamelCase							 =  torch.stack([row['''label'''] for row in batch]	)
					_UpperCamelCase							 =  torch.stack([row['''image_start_token'''] for row in batch]	)
					_UpperCamelCase							 =  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__		(			)							->		Dict:


					"""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__		(			)							->		List[str]:


					"""simple docstring"""





					return transforms.Compose(
					    [
					        transforms.Resize(2_56	),
					        transforms.CenterCrop(2_24	),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46777044, 0.44531429, 0.40661017],     std=[0.12221994, 0.12145835, 0.14380469],     ),
					    ]	)








"""simple docstring"""





from __future__ import annotations



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		float:


					"""simple docstring"""





					_UpperCamelCase							 =  sorted(numsa + numsa	)
					_UpperCamelCase ,  _UpperCamelCase							 =  divmod(len(__snake_case	),     2	)
					if mod == 1:
										return all_numbers[div]
					else:
										return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				_a														=		[float(x) for x in input("""Enter the elements of first array: """).split()]
				_a														=		[float(x) for x in input("""Enter the elements of second array: """).split()]
				print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")






