
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


_lowercase											=    {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						,       __A				)						->    Optional[int]:
              super().__init__()
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torchvision.models.resnetaaa(pretrained=__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=list(model.children()				)[:-2]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=nn.Sequential(*__A				)
              SCREAMING_SNAKE_CASE_							:				str		=nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]				)





       def 	_snake_case				(    self						,       __A				)						->    int:
              # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.pool(self.model(__A				)				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.flatten(__A						,       start_dim=2				)
              SCREAMING_SNAKE_CASE_							:				Dict		=out.transpose(1						,       2				).contiguous()
              return out  # BxNx2048






class 			lowercase_		(      A       ):


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

              SCREAMING_SNAKE_CASE_							:				Any		=transforms


       def __len__(    self				)						->    Optional[Any]:
              return len(self.data				)


       def __getitem__(    self						,       __A				)						->    str:
              SCREAMING_SNAKE_CASE_							:				str		=torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']						,       add_special_tokens=__A				)				)
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[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_							:				Any		=1

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=Image.open(os.path.join(self.data_dir						,       self.data[index]['''img''']				)				).convert('''RGB'''				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.transforms(__A				)

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





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






def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Union[str, Any]				)			->						str:
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=[len(row['''sentence''']				) for row in batch]
       SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				str		=len(UpperCAmelCase_				), max(UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				Any		=torch.zeros(UpperCAmelCase_			,							UpperCAmelCase_			,							dtype=torch.long				)
       SCREAMING_SNAKE_CASE_							:				str		=torch.zeros(UpperCAmelCase_			,							UpperCAmelCase_			,							dtype=torch.long				)

       for i_batch, (input_row, length) in enumerate(zip(UpperCAmelCase_			,							UpperCAmelCase_				)				):
              SCREAMING_SNAKE_CASE_							:				Dict		=input_row['''sentence''']
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=1

       SCREAMING_SNAKE_CASE_							:				int		=torch.stack([row['''image'''] for row in batch]				)
       SCREAMING_SNAKE_CASE_							:				Any		=torch.stack([row['''label'''] for row in batch]				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.stack([row['''image_start_token'''] for row in batch]				)
       SCREAMING_SNAKE_CASE_							:				Tuple		=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 							SCREAMING_SNAKE_CASE_	(					)			->						str:
       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 							SCREAMING_SNAKE_CASE_	(					)			->						List[Any]:
       return transforms.Compose(
           [
               transforms.Resize(2_5_6				),
               transforms.CenterCrop(2_2_4				),
               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]			,							),
           ]				)


from collections.abc import Sequence
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Sequence[int] | None = None				)			->						int:
       if nums is None or not nums:
              raise ValueError('''Input sequence should not be empty'''				)

       SCREAMING_SNAKE_CASE_							:				Tuple		=nums[0]
       for i in range(1			,							len(UpperCAmelCase_				)				):
              SCREAMING_SNAKE_CASE_							:				Any		=nums[i]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=max(UpperCAmelCase_			,							ans + num			,							UpperCAmelCase_				)

       return ans


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Try on a sample input from the user
 _lowercase											=    int(input("""Enter number of elements : """).strip())
 _lowercase											=    list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
 print(max_subsequence_sum(array))
