


'''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	:	Tuple     =     {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		:Optional[int]  ,_UpperCamelCase		:List[Any]			):
									super().__init__()
									snake_case_		:							List[Any]			       =						torchvision.models.resnetaaa(pretrained=_UpperCamelCase			)
									snake_case_		:							Dict			       =						list(model.children()			)[:-2]
									snake_case_		:							Tuple			       =						nn.Sequential(*_UpperCamelCase			)
									snake_case_		:							Any			       =						nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]			)





							def 		a__   (  self		:int  ,_UpperCamelCase		:int			):
									# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
									snake_case_		:							Dict			       =						self.pool(self.model(_UpperCamelCase			)			)
									snake_case_		:							str			       =						torch.flatten(_UpperCamelCase  ,start_dim=2			)
									snake_case_		:							int			       =						out.transpose(1  ,2			).contiguous()
									return out  # BxNx2048





class 						__UpperCamelCase	(						lowercase__							):





							def __init__(  self		:Tuple  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:Any			):
									snake_case_		:							int			       =						[json.loads(_UpperCamelCase			) for l in open(_UpperCamelCase			)]
									snake_case_		:							Dict			       =						os.path.dirname(_UpperCamelCase			)
									snake_case_		:							Optional[int]			       =						tokenizer
									snake_case_		:							Optional[Any]			       =						labels
									snake_case_		:							List[Any]			       =						len(_UpperCamelCase			)
									snake_case_		:							Optional[Any]			       =						max_seq_length

									snake_case_		:							List[Any]			       =						transforms





							def __len__(  self		:Union[str, Any]			):
									return len(self.data			)





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

									snake_case_		:							int			       =						torch.zeros(self.n_classes			)
									snake_case_		:							Tuple			       =						1

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

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





							def 		a__   (  self		:Dict			):
									snake_case_		:							Tuple			       =						Counter()
									for row in self.data:
											label_freqs.update(row["""label"""]			)
									return label_freqs





def      UpperCAmelCase      (		lowerCamelCase_  :str ):







		'''simple docstring'''
		snake_case_		:							Optional[int]			       =						[len(row["""sentence"""] ) for row in batch]
		snake_case_       ,      snake_case_		:							Optional[Any]			       =						len(lowerCamelCase_ ), max(lowerCamelCase_ )

		snake_case_		:							Optional[Any]			       =						torch.zeros(lowerCamelCase_				,      lowerCamelCase_				,      dtype=torch.long )
		snake_case_		:							Tuple			       =						torch.zeros(lowerCamelCase_				,      lowerCamelCase_				,      dtype=torch.long )

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

		snake_case_		:							str			       =						torch.stack([row["""image"""] for row in batch] )
		snake_case_		:							Optional[Any]			       =						torch.stack([row["""label"""] for row in batch] )
		snake_case_		:							Any			       =						torch.stack([row["""image_start_token"""] for row in batch] )
		snake_case_		:							int			       =						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      (		):







		'''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      (		):







		'''simple docstring'''
		return transforms.Compose(
		    [
		        transforms.Resize(2_56 ),
		        transforms.CenterCrop(2_24 ),
		        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'''






import math





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						len(lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
		snake_case_		:							str			       =						0
		while arr[min(lowerCamelCase_				,      lowerCamelCase_ ) - 1] < x:
				snake_case_		:							Any			       =						step
				step += int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
				if prev >= n:
						return -1

		while arr[prev] < x:
				snake_case_		:							Optional[Any]			       =						prev + 1
				if prev == min(lowerCamelCase_				,      lowerCamelCase_ ):
						return -1
		if arr[prev] == x:
				return prev
		return -1


if __name__ == "__main__":
							__A	:	Any     =     input('Enter numbers separated by a comma:\n').strip()
							__A	:	Union[str, Any]     =     [int(item) for item in user_input.split(',')]
							__A	:	Optional[int]     =     int(input('Enter the number to be searched:\n'))
							__A	:	int     =     jump_search(arr, x)
							if res == -1:
														print('Number not found!')
							else:
														print(F'Number {x} is at index {res}')