



'''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


UpperCamelCase__       :	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 						_UpperCamelCase  (							nn.Module    ):


			'''simple docstring'''

			def __init__(							self     : Optional[Any]							,     lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






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



			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : Any						):





					"""simple docstring"""






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






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		[json.loads(lowerCAmelCase__						) for l in open(lowerCAmelCase__						)]
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		os.path.dirname(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		tokenizer
					__SCREAMING_SNAKE_CASE       :		Any           =		labels
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		len(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		max_seq_length

					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		transforms

			def __len__(							self     : Dict						):





					"""simple docstring"""






					return len(self.data						)

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





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE       :		Tuple           =		torch.zeros(self.n_classes						)
					__SCREAMING_SNAKE_CASE       :		str           =		1

					__SCREAMING_SNAKE_CASE       :		Dict           =		Image.open(os.path.join(self.data_dir							,     self.data[index]["""img"""]						)						).convert("""RGB"""						)
					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.transforms(lowerCAmelCase__						)

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



			def UpperCamelCase__					(							self     : List[str]						):





					"""simple docstring"""






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


def 				lowerCAmelCase_   (	_lowerCamelCase:   Union[str, Any]					):
		__SCREAMING_SNAKE_CASE       :		int           =		[len(row["""sentence"""]					) for row in batch]
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		List[Any]           =		len(_lowerCamelCase					), max(_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		str           =		torch.zeros(_lowerCamelCase       ,      _lowerCamelCase       ,      dtype=torch.long					)
		__SCREAMING_SNAKE_CASE       :		str           =		torch.zeros(_lowerCamelCase       ,      _lowerCamelCase       ,      dtype=torch.long					)

		for i_batch, (input_row, length) in enumerate(zip(_lowerCamelCase       ,      _lowerCamelCase					)					):
				__SCREAMING_SNAKE_CASE       :		int           =		input_row["""sentence"""]
				__SCREAMING_SNAKE_CASE       :		Tuple           =		1

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		torch.stack([row["""image"""] for row in batch]					)
		__SCREAMING_SNAKE_CASE       :		int           =		torch.stack([row["""label"""] for row in batch]					)
		__SCREAMING_SNAKE_CASE       :		str           =		torch.stack([row["""image_start_token"""] for row in batch]					)
		__SCREAMING_SNAKE_CASE       :		Dict           =		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_   (	):
		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_   (	):
		return transforms.Compose(
		    [
		        transforms.Resize(2_56					),
		        transforms.CenterCrop(2_24					),
		        transforms.ToTensor(),
		        transforms.Normalize(
		            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]       ,      std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]       ,      ),
		    ]					)




'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		str           =		int(_lowerCamelCase					)
		if n_element < 1:
				__SCREAMING_SNAKE_CASE       :		List[str]           =		ValueError("""a should be a positive number"""					)
				raise my_error

		__SCREAMING_SNAKE_CASE       :		List[Any]           =		[1]
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[int]           =		(0, 0, 0)
		__SCREAMING_SNAKE_CASE       :		List[str]           =		1
		while index < n_element:
				while hamming_list[i] * 2 <= hamming_list[-1]:
						i += 1
				while hamming_list[j] * 3 <= hamming_list[-1]:
						j += 1
				while hamming_list[k] * 5 <= hamming_list[-1]:
						k += 1
				hamming_list.append(
				    min(hamming_list[i] * 2       ,      hamming_list[j] * 3       ,      hamming_list[k] * 5					)					)
				index += 1
		return hamming_list


if __name__ == "__main__":
						UpperCamelCase__       :	Optional[Any]    = input('''Enter the last number (nth term) of the Hamming Number Series: ''')
						print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
						UpperCamelCase__       :	List[str]    = hamming(int(n))
						print('''-----------------------------------------------------''')
						print(f"The list with nth numbers is: {hamming_numbers}")
						print('''-----------------------------------------------------''')