




"""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   :  List[Any]					=      {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       lowerCAmelCase__       (			nn.Module    ):


				"""simple docstring"""





				def __init__(     self					:	Optional[int]    ,   lowercase__					:	Any						):
						super().__init__()
						__lowercase				:		Union[str, Any]												=				torchvision.models.resnetaaa(pretrained=lowercase__						)
						__lowercase				:		List[str]												=				list(model.children()						)[:-2]
						__lowercase				:		Optional[Any]												=				nn.Sequential(*lowercase__						)
						__lowercase				:		Union[str, Any]												=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]						)



				def       snake_case    (     self					:	Optional[int]    ,   lowercase__					:	Optional[Any]						):
						# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
						__lowercase				:		str												=				self.pool(self.model(lowercase__						)						)
						__lowercase				:		Optional[Any]												=				torch.flatten(lowercase__    ,   start_dim=2						)
						__lowercase				:		Any												=				out.transpose(1    ,   2						).contiguous()
						return out  # BxNx2048





class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





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

						__lowercase				:		Optional[Any]												=				transforms



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



				def __getitem__(     self					:	int    ,   lowercase__					:	List[Any]						):
						__lowercase				:		List[str]												=				torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]    ,   add_special_tokens=lowercase__						)						)
						__lowercase						,__lowercase						,__lowercase				:		Dict												=				sentence[0], sentence[1:-1], sentence[-1]
						__lowercase				:		int												=				sentence[: self.max_seq_length]

						__lowercase				:		Union[str, Any]												=				torch.zeros(self.n_classes						)
						__lowercase				:		List[str]												=				1

						__lowercase				:		str												=				Image.open(os.path.join(self.data_dir    ,   self.data[index]["img"]						)						).convert("RGB"						)
						__lowercase				:		str												=				self.transforms(lowercase__						)

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



				def       snake_case    (     self					:	str						):
						__lowercase				:		Optional[int]												=				Counter()
						for row in self.data:
								label_freqs.update(row["label"]						)
						return label_freqs





def 						snake_case__       (					_lowerCamelCase				)			->List[Any]:






		"""simple docstring"""


		__lowercase				:		Optional[Any]												=				[len(row["sentence"]				) for row in batch]
		__lowercase						,__lowercase				:		Union[str, Any]												=				len(_lowerCamelCase				), max(_lowerCamelCase				)

		__lowercase				:		str												=				torch.zeros(_lowerCamelCase,  _lowerCamelCase,  dtype=torch.long				)
		__lowercase				:		int												=				torch.zeros(_lowerCamelCase,  _lowerCamelCase,  dtype=torch.long				)

		for i_batch, (input_row, length) in enumerate(zip(_lowerCamelCase,  _lowerCamelCase				)				):
				__lowercase				:		Optional[Any]												=				input_row["sentence"]
				__lowercase				:		Optional[Any]												=				1

		__lowercase				:		Any												=				torch.stack([row["image"] for row in batch]				)
		__lowercase				:		Dict												=				torch.stack([row["label"] for row in batch]				)
		__lowercase				:		Any												=				torch.stack([row["image_start_token"] for row in batch]				)
		__lowercase				:		Optional[Any]												=				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__       (					)			->Tuple:






		"""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 						snake_case__       (					)			->Dict:






		"""simple docstring"""


		return transforms.Compose(
		    [
		        transforms.Resize(2_56				),
		        transforms.CenterCrop(2_24				),
		        transforms.ToTensor(),
		        transforms.Normalize(
		            mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7],  std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9],  ),
		    ]				)






"""simple docstring"""







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  )			->list[float]:






		"""simple docstring"""


		__lowercase						,__lowercase				:		Dict												=				coefficient_matrix.shape
		__lowercase						,__lowercase				:		Tuple												=				constant_matrix.shape

		if rowsa != colsa:
				__lowercase				:		str												=				F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
				raise ValueError(_lowerCamelCase				)

		if colsa != 1:
				__lowercase				:		Dict												=				F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
				raise ValueError(_lowerCamelCase				)

		if rowsa != rowsa:
				__lowercase				:		Optional[Any]												=				(
				    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
				    F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
				)
				raise ValueError(_lowerCamelCase				)

		if len(_lowerCamelCase				) != rowsa:
				__lowercase				:		Dict												=				(
				    "Number of initial values must be equal to number of rows in coefficient "
				    F'matrix but received {len(_lowerCamelCase				)} and {rowsa}'
				)
				raise ValueError(_lowerCamelCase				)

		if iterations <= 0:
				raise ValueError("Iterations must be at least 1"				)

		__lowercase				:		NDArray[floataa]												=				np.concatenate(
		    (coefficient_matrix, constant_matrix),  axis=1				)

		__lowercase						,__lowercase				:		str												=				table.shape

		strictly_diagonally_dominant(_lowerCamelCase				)

		# Iterates the whole matrix for given number of times
		for _ in range(_lowerCamelCase				):
				__lowercase				:		List[Any]												=				[]
				for row in range(_lowerCamelCase				):
						__lowercase				:		Optional[Any]												=				0
						for col in range(_lowerCamelCase				):
								if col == row:
										__lowercase				:		Tuple												=				table[row][col]
								elif col == cols - 1:
										__lowercase				:		Tuple												=				table[row][col]
								else:
										temp += (-1) * table[row][col] * init_val[col]
						__lowercase				:		List[Any]												=				(temp + val) / denom
						new_val.append(_lowerCamelCase				)
				__lowercase				:		Dict												=				new_val

		return [float(_lowerCamelCase				) for i in new_val]

def 						snake_case__       (					_lowerCamelCase				)			->bool:






		"""simple docstring"""


		__lowercase						,__lowercase				:		int												=				table.shape

		__lowercase				:		Optional[int]												=				True

		for i in range(0,  _lowerCamelCase				):
				__lowercase				:		Optional[int]												=				0
				for j in range(0,  cols - 1				):
						if i == j:
								continue
						else:
								total += table[i][j]

				if table[i][i] <= total:
						raise ValueError("Coefficient matrix is not strictly diagonally dominant"				)

		return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
						import doctest

						doctest.testmod()
