


'''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					       =							{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      ,					lowerCamelCase_     ) ->							Dict:
														super().__init__()
														lowerCAmelCase__      =			torchvision.models.resnetaaa(pretrained=UpperCamelCase__     )
														lowerCAmelCase__      =			list(model.children()     )[:-2]
														lowerCAmelCase__      =			nn.Sequential(*UpperCamelCase__     )
														lowerCAmelCase__      =			nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Dict:
														lowerCAmelCase__      =			self.pool(self.model(UpperCamelCase__     )     )
														lowerCAmelCase__      =			torch.flatten(UpperCamelCase__      ,					start_dim=2     )
														lowerCAmelCase__      =			out.transpose(1      ,					2     ).contiguous()
														return out  # BxNx2048




class 	a__       (							lowercase_						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							List[str]:
														lowerCAmelCase__      =			[json.loads(UpperCamelCase__     ) for l in open(UpperCamelCase__     )]
														lowerCAmelCase__      =			os.path.dirname(UpperCamelCase__     )
														lowerCAmelCase__      =			tokenizer
														lowerCAmelCase__      =			labels
														lowerCAmelCase__      =			len(UpperCamelCase__     )
														lowerCAmelCase__      =			max_seq_length

														lowerCAmelCase__      =			transforms







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







							def __getitem__(	self      ,					lowerCamelCase_     ) ->							Optional[int]:
														lowerCAmelCase__      =			torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']      ,					add_special_tokens=UpperCamelCase__     )     )
														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(UpperCamelCase__     )

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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														lowerCAmelCase__      =			Counter()
														for row in self.data:
																					label_freqs.update(row['''label''']     )
														return label_freqs


def       _snake_case     (  A )		->					str:
							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       _snake_case     (  )		->					int:
							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       _snake_case     (  )		->					List[str]:
							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'''






def       _snake_case     (  A )		->					int:
							return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))


def       _snake_case     (  A )		->					bool:
							lowerCAmelCase__      =			0
							lowerCAmelCase__      =			number
							while duplicate > 0:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			divmod(A				,					10 )
														fact_sum += factorial(A )
							return fact_sum == number


if __name__ == "__main__":
		print('''Program to check whether a number is a Krisnamurthy Number or not.''')
		__UpperCAmelCase					       =							int(input('''Enter number: ''').strip())
		print(
		    f"""{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."""
		)