




"""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				:		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				:		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 math

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->list:






		"""simple docstring"""


		if len(_lowerCamelCase				) != 2 or len(a[0]				) != 2 or len(_lowerCamelCase				) != 2 or len(b[0]				) != 2:
				raise Exception("Matrices are not 2x2"				)
		__lowercase				:		Tuple												=				[
		    [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
		    [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
		]
		return new_matrix

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Dict:






		"""simple docstring"""


		return [
		    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]				)				)]
		    for row in range(len(_lowerCamelCase				)				)
		]

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Tuple:






		"""simple docstring"""


		return [
		    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]				)				)]
		    for row in range(len(_lowerCamelCase				)				)
		]

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






		"""simple docstring"""


		if len(_lowerCamelCase				) % 2 != 0 or len(a[0]				) % 2 != 0:
				raise Exception("Odd matrices are not supported!"				)

		__lowercase				:		Tuple												=				len(_lowerCamelCase				)
		__lowercase				:		Any												=				matrix_length // 2

		__lowercase				:		Dict												=				[[a[i][j] for j in range(_lowerCamelCase,  _lowerCamelCase				)] for i in range(_lowerCamelCase				)]
		__lowercase				:		List[str]												=				[
		    [a[i][j] for j in range(_lowerCamelCase,  _lowerCamelCase				)] for i in range(_lowerCamelCase,  _lowerCamelCase				)
		]

		__lowercase				:		Union[str, Any]												=				[[a[i][j] for j in range(_lowerCamelCase				)] for i in range(_lowerCamelCase				)]
		__lowercase				:		List[str]												=				[[a[i][j] for j in range(_lowerCamelCase				)] for i in range(_lowerCamelCase,  _lowerCamelCase				)]

		return top_left, top_right, bot_left, bot_right

def 						snake_case__       (					_lowerCamelCase				)			->tuple[int, int]:






		"""simple docstring"""


		return len(_lowerCamelCase				), len(matrix[0]				)

def 						snake_case__       (					_lowerCamelCase				)			->None:






		"""simple docstring"""


		print("\n".join(str(_lowerCamelCase				) for line in matrix				)				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->list:






		"""simple docstring"""


		if matrix_dimensions(_lowerCamelCase				) == (2, 2):
				return default_matrix_multiplication(_lowerCamelCase,  _lowerCamelCase				)

		__lowercase						,__lowercase						,__lowercase						,__lowercase				:		Optional[Any]												=				split_matrix(_lowerCamelCase				)
		__lowercase						,__lowercase						,__lowercase						,__lowercase				:		Any												=				split_matrix(_lowerCamelCase				)

		__lowercase				:		str												=				actual_strassen(_lowerCamelCase,  matrix_subtraction(_lowerCamelCase,  _lowerCamelCase				)				)
		__lowercase				:		Dict												=				actual_strassen(matrix_addition(_lowerCamelCase,  _lowerCamelCase				),  _lowerCamelCase				)
		__lowercase				:		Union[str, Any]												=				actual_strassen(matrix_addition(_lowerCamelCase,  _lowerCamelCase				),  _lowerCamelCase				)
		__lowercase				:		Any												=				actual_strassen(_lowerCamelCase,  matrix_subtraction(_lowerCamelCase,  _lowerCamelCase				)				)
		__lowercase				:		List[Any]												=				actual_strassen(matrix_addition(_lowerCamelCase,  _lowerCamelCase				),  matrix_addition(_lowerCamelCase,  _lowerCamelCase				)				)
		__lowercase				:		Optional[int]												=				actual_strassen(matrix_subtraction(_lowerCamelCase,  _lowerCamelCase				),  matrix_addition(_lowerCamelCase,  _lowerCamelCase				)				)
		__lowercase				:		Optional[Any]												=				actual_strassen(matrix_subtraction(_lowerCamelCase,  _lowerCamelCase				),  matrix_addition(_lowerCamelCase,  _lowerCamelCase				)				)

		__lowercase				:		str												=				matrix_addition(matrix_subtraction(matrix_addition(_lowerCamelCase,  _lowerCamelCase				),  _lowerCamelCase				),  _lowerCamelCase				)
		__lowercase				:		Dict												=				matrix_addition(_lowerCamelCase,  _lowerCamelCase				)
		__lowercase				:		int												=				matrix_addition(_lowerCamelCase,  _lowerCamelCase				)
		__lowercase				:		Union[str, Any]												=				matrix_subtraction(matrix_subtraction(matrix_addition(_lowerCamelCase,  _lowerCamelCase				),  _lowerCamelCase				),  _lowerCamelCase				)

		# construct the new matrix from our 4 quadrants
		__lowercase				:		Optional[int]												=				[]
		for i in range(len(_lowerCamelCase				)				):
				new_matrix.append(top_left[i] + top_right[i]				)
		for i in range(len(_lowerCamelCase				)				):
				new_matrix.append(bot_left[i] + bot_right[i]				)
		return new_matrix

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->list:






		"""simple docstring"""


		if matrix_dimensions(_lowerCamelCase				)[1] != matrix_dimensions(_lowerCamelCase				)[0]:
				__lowercase				:		int												=				(
				    "Unable to multiply these matrices, please check the dimensions.\n"
				    F'Matrix A: {matrixa}\n'
				    F'Matrix B: {matrixa}'
				)
				raise Exception(_lowerCamelCase				)
		__lowercase				:		List[Any]												=				matrix_dimensions(_lowerCamelCase				)
		__lowercase				:		Tuple												=				matrix_dimensions(_lowerCamelCase				)

		if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
				return [matrixa, matrixa]

		__lowercase				:		int												=				max(*_lowerCamelCase,  *_lowerCamelCase				)
		__lowercase				:		Any												=				int(math.pow(2,  math.ceil(math.loga(_lowerCamelCase				)				)				)				)
		__lowercase				:		Dict												=				matrixa
		__lowercase				:		str												=				matrixa

		# Adding zeros to the matrices so that the arrays dimensions are the same and also
		# power of 2
		for i in range(0,  _lowerCamelCase				):
				if i < dimensiona[0]:
						for _ in range(dimensiona[1],  _lowerCamelCase				):
								new_matrixa[i].append(0				)
				else:
						new_matrixa.append([0] * maxim				)
				if i < dimensiona[0]:
						for _ in range(dimensiona[1],  _lowerCamelCase				):
								new_matrixa[i].append(0				)
				else:
						new_matrixa.append([0] * maxim				)

		__lowercase				:		Dict												=				actual_strassen(_lowerCamelCase,  _lowerCamelCase				)

		# Removing the additional zeros
		for i in range(0,  _lowerCamelCase				):
				if i < dimensiona[0]:
						for _ in range(dimensiona[1],  _lowerCamelCase				):
								final_matrix[i].pop()
				else:
						final_matrix.pop()
		return final_matrix


if __name__ == "__main__":
						__A   :  int					=      [
						    [2, 3, 4, 5],
						    [6, 4, 3, 1],
						    [2, 3, 6, 7],
						    [3, 1, 2, 4],
						    [2, 3, 4, 5],
						    [6, 4, 3, 1],
						    [2, 3, 6, 7],
						    [3, 1, 2, 4],
						    [2, 3, 4, 5],
						    [6, 2, 3, 1],
						]
						__A   :  Union[str, Any]					=      [[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
						print(strassen(matrixa, matrixa))
