



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


lowerCAmelCase__		:					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	):

						'''simple docstring'''




						def __init__(   self				,							_A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

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







class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A				,							_A				,							_A				,							_A		):







													'''simple docstring'''

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

													__SCREAMING_SNAKE_CASE									= transforms






						def __len__(   self		):







													'''simple docstring'''

													return len(self.data		)






						def __getitem__(   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= torch.LongTensor(self.tokenizer.encode(self.data[index]['text']				,							add_special_tokens=_A		)		)
													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= sentence[0], sentence[1:-1], sentence[-1]
													__SCREAMING_SNAKE_CASE									= sentence[: self.max_seq_length]

													__SCREAMING_SNAKE_CASE									= torch.zeros(self.n_classes		)
													__SCREAMING_SNAKE_CASE									= 1

													__SCREAMING_SNAKE_CASE									= Image.open(os.path.join(self.data_dir				,							self.data[index]['img']		)		).convert('RGB'		)
													__SCREAMING_SNAKE_CASE									= self.transforms(_A		)

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






						def 	_A     (   self		):







													'''simple docstring'''

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







def      __lowercase       (			a__       )    ->			int:
							__SCREAMING_SNAKE_CASE									= [len(row['sentence']       ) for row in batch]
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= len(a__       ), max(a__       )

							__SCREAMING_SNAKE_CASE									= torch.zeros(a__							,       a__							,       dtype=torch.long       )
							__SCREAMING_SNAKE_CASE									= torch.zeros(a__							,       a__							,       dtype=torch.long       )

							for i_batch, (input_row, length) in enumerate(zip(a__							,       a__       )       ):
														__SCREAMING_SNAKE_CASE									= input_row['sentence']
														__SCREAMING_SNAKE_CASE									= 1

							__SCREAMING_SNAKE_CASE									= torch.stack([row['image'] for row in batch]       )
							__SCREAMING_SNAKE_CASE									= torch.stack([row['label'] for row in batch]       )
							__SCREAMING_SNAKE_CASE									= torch.stack([row['image_start_token'] for row in batch]       )
							__SCREAMING_SNAKE_CASE									= 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      __lowercase       (			)    ->			Dict:
							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      __lowercase       (			)    ->			List[str]:
							return transforms.Compose(
							    [
							        transforms.Resize(2_56       ),
							        transforms.CenterCrop(2_24       ),
							        transforms.ToTensor(),
							        transforms.Normalize(
							            mean=[0.46777044, 0.44531429, 0.40661017]							,       std=[0.12221994, 0.12145835, 0.14380469]							,       ),
							    ]       )











import os






def      __lowercase       (			a__ = "input.txt"       )    ->			int:

							with open(os.path.join(os.path.dirname(a__       )							,       a__       )       ) as input_file:
														__SCREAMING_SNAKE_CASE									= [
														    [int(a__       ) for element in line.split(','       )]
														    for line in input_file.readlines()
														]

							__SCREAMING_SNAKE_CASE									= len(a__       )
							__SCREAMING_SNAKE_CASE									= len(matrix[0]       )

							__SCREAMING_SNAKE_CASE									= [[-1 for _ in range(a__       )] for _ in range(a__       )]
							for i in range(a__       ):
														__SCREAMING_SNAKE_CASE									= matrix[i][0]

							for j in range(1							,       a__       ):
														for i in range(a__       ):
																					__SCREAMING_SNAKE_CASE									= minimal_path_sums[i][j - 1] + matrix[i][j]

														for i in range(1							,       a__       ):
																					__SCREAMING_SNAKE_CASE									= min(
																					    minimal_path_sums[i][j]							,       minimal_path_sums[i - 1][j] + matrix[i][j]       )

														for i in range(rows - 2							,       -1							,       -1       ):
																					__SCREAMING_SNAKE_CASE									= min(
																					    minimal_path_sums[i][j]							,       minimal_path_sums[i + 1][j] + matrix[i][j]       )

							return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums       )


if __name__ == "__main__":
			print(F'''{solution() = }''')






