


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     lowercase__  (					nn.Module		):






  def __init__(					self      :					Union[str, Any]		,						_lowercase      :					Dict ):




         """simple docstring"""







         super().__init__()
         UpperCAmelCase__										=     torchvision.models.resnetaaa(pretrained=_lowercase )
         UpperCAmelCase__										=     list(model.children() )[:-2]
         UpperCAmelCase__										=     nn.Sequential(*_lowercase )
         UpperCAmelCase__										=     nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						_lowercase      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     self.pool(self.model(_lowercase ) )
         UpperCAmelCase__										=     torch.flatten(_lowercase		,						start_dim=2 )
         UpperCAmelCase__										=     out.transpose(1		,						2 ).contiguous()
         return out  # BxNx2048




class     lowercase__  (					UpperCamelCase_		):






  def __init__(					self      :					int		,						_lowercase      :					str		,						_lowercase      :					int		,						_lowercase      :					Any		,						_lowercase      :					List[str]		,						_lowercase      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     [json.loads(_lowercase ) for l in open(_lowercase )]
         UpperCAmelCase__										=     os.path.dirname(_lowercase )
         UpperCAmelCase__										=     tokenizer
         UpperCAmelCase__										=     labels
         UpperCAmelCase__										=     len(_lowercase )
         UpperCAmelCase__										=     max_seq_length

         UpperCAmelCase__										=     transforms






  def __len__(					self      :					List[str] ):




         """simple docstring"""







         return len(self.data )






  def __getitem__(					self      :					Optional[int]		,						_lowercase      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]		,						add_special_tokens=_lowercase ) )
         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(_lowercase )

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






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







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





def __UpperCAmelCase       (		__A			)						->					Tuple:



       '''simple docstring'''





       UpperCAmelCase__										=     [len(row["sentence"]			) for row in batch]
       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 __UpperCAmelCase       (		)						->					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 __UpperCAmelCase       (		)						->					Dict:



       '''simple docstring'''





       return transforms.Compose(
           [
               transforms.Resize(2_5_6			),
               transforms.CenterCrop(2_2_4			),
               transforms.ToTensor(),
               transforms.Normalize(
                   mean=[0.46777044, 0.44531429, 0.40661017] ,						std=[0.12221994, 0.12145835, 0.14380469] ,						),
           ]			)









from __future__ import annotations

import math





def __UpperCAmelCase       (		__A ,						__A			)						->					float:



       '''simple docstring'''





       UpperCAmelCase__										=     u
       for i in range(1 ,						__A			):
              UpperCAmelCase__										=     temp * (u - i)
       return temp





def __UpperCAmelCase       (		)						->					None:



       '''simple docstring'''





       UpperCAmelCase__										=     int(input("enter the numbers of values: "			)			)
       UpperCAmelCase__										=     []
       for _ in range(__A			):
              y.append([]			)
       for i in range(__A			):
              for j in range(__A			):
                     y[i].append(__A			)
                     UpperCAmelCase__										=     0

       print("enter the values of parameters in a list: "			)
       UpperCAmelCase__										=     list(map(__A ,						input().split()			)			)

       print("enter the values of corresponding parameters: "			)
       for i in range(__A			):
              UpperCAmelCase__										=     float(input()			)

       UpperCAmelCase__										=     int(input("enter the value to interpolate: "			)			)
       UpperCAmelCase__										=     (value - x[0]) / (x[1] - x[0])

       # for calculating forward difference table

       for i in range(1 ,						__A			):
              for j in range(n - i			):
                     UpperCAmelCase__										=     y[j + 1][i - 1] - y[j][i - 1]

       UpperCAmelCase__										=     y[0][0]
       for i in range(1 ,						__A			):
              summ += (ucal(__A ,						__A			) * y[0][i]) / math.factorial(__A			)

       print(F"""the value at {value} is {summ}"""			)


if __name__ == "__main__":
    main()





