





from __future__ import annotations

import math
def 							a_	(    __magic_name__		,     __magic_name__   )				->			Dict:




				"""simple docstring"""






				if len(_snake_case   ) != 2 or len(a[0]   ) != 2 or len(_snake_case   ) != 2 or len(b[0]   ) != 2:
								raise Exception('''Matrices are not 2x2'''   )
				snake_case   :					Optional[Any]    						=				[
				    [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 							a_	(    __magic_name__		,     __magic_name__   )				->			List[str]:




				"""simple docstring"""






				return [
				    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]   )   )]
				    for row in range(len(_snake_case   )   )
				]
def 							a_	(    __magic_name__		,     __magic_name__   )				->			Union[str, Any]:




				"""simple docstring"""






				return [
				    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]   )   )]
				    for row in range(len(_snake_case   )   )
				]
def 							a_	(    __magic_name__   )				->			Any:




				"""simple docstring"""






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

				snake_case   :					List[Any]    						=				len(_snake_case   )
				snake_case   :					str    						=				matrix_length // 2

				snake_case   :					Dict    						=				[[a[i][j] for j in range(_snake_case		,     _snake_case   )] for i in range(_snake_case   )]
				snake_case   :					Optional[int]    						=				[
				    [a[i][j] for j in range(_snake_case		,     _snake_case   )] for i in range(_snake_case		,     _snake_case   )
				]

				snake_case   :					Dict    						=				[[a[i][j] for j in range(_snake_case   )] for i in range(_snake_case   )]
				snake_case   :					str    						=				[[a[i][j] for j in range(_snake_case   )] for i in range(_snake_case		,     _snake_case   )]

				return top_left, top_right, bot_left, bot_right
def 							a_	(    __magic_name__   )				->			Dict:




				"""simple docstring"""






				return len(_snake_case   ), len(matrix[0]   )
def 							a_	(    __magic_name__   )				->			List[str]:




				"""simple docstring"""






				print('''\n'''.join(str(_snake_case   ) for line in matrix   )   )
def 							a_	(    __magic_name__		,     __magic_name__   )				->			int:




				"""simple docstring"""






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

				snake_case						,						snake_case						,						snake_case						,						snake_case   :					List[Any]    						=				split_matrix(_snake_case   )
				snake_case						,						snake_case						,						snake_case						,						snake_case   :					List[str]    						=				split_matrix(_snake_case   )

				snake_case   :					Optional[int]    						=				actual_strassen(_snake_case		,     matrix_subtraction(_snake_case		,     _snake_case   )   )
				snake_case   :					int    						=				actual_strassen(matrix_addition(_snake_case		,     _snake_case   )		,     _snake_case   )
				snake_case   :					Optional[Any]    						=				actual_strassen(matrix_addition(_snake_case		,     _snake_case   )		,     _snake_case   )
				snake_case   :					Dict    						=				actual_strassen(_snake_case		,     matrix_subtraction(_snake_case		,     _snake_case   )   )
				snake_case   :					Optional[Any]    						=				actual_strassen(matrix_addition(_snake_case		,     _snake_case   )		,     matrix_addition(_snake_case		,     _snake_case   )   )
				snake_case   :					Optional[Any]    						=				actual_strassen(matrix_subtraction(_snake_case		,     _snake_case   )		,     matrix_addition(_snake_case		,     _snake_case   )   )
				snake_case   :					Dict    						=				actual_strassen(matrix_subtraction(_snake_case		,     _snake_case   )		,     matrix_addition(_snake_case		,     _snake_case   )   )

				snake_case   :					Optional[int]    						=				matrix_addition(matrix_subtraction(matrix_addition(_snake_case		,     _snake_case   )		,     _snake_case   )		,     _snake_case   )
				snake_case   :					Union[str, Any]    						=				matrix_addition(_snake_case		,     _snake_case   )
				snake_case   :					Dict    						=				matrix_addition(_snake_case		,     _snake_case   )
				snake_case   :					List[str]    						=				matrix_subtraction(matrix_subtraction(matrix_addition(_snake_case		,     _snake_case   )		,     _snake_case   )		,     _snake_case   )

				# construct the new matrix from our 4 quadrants
				snake_case   :					Tuple    						=				[]
				for i in range(len(_snake_case   )   ):
								new_matrix.append(top_left[i] + top_right[i]   )
				for i in range(len(_snake_case   )   ):
								new_matrix.append(bot_left[i] + bot_right[i]   )
				return new_matrix
def 							a_	(    __magic_name__		,     __magic_name__   )				->			Optional[Any]:




				"""simple docstring"""






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

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

				snake_case   :					List[str]    						=				max(*_snake_case		,     *_snake_case   )
				snake_case   :					Any    						=				int(math.pow(2		,     math.ceil(math.loga(_snake_case   )   )   )   )
				snake_case   :					Any    						=				matrixa
				snake_case   :					Union[str, Any]    						=				matrixa

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

				snake_case   :					Tuple    						=				actual_strassen(_snake_case		,     _snake_case   )

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


if __name__ == "__main__":
			_a     :    str         =						[
			    [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     :    int         =						[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
			print(strassen(matrixa, matrixa))





'''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


SCREAMING_SNAKE_CASE__          =  {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 					snake_case			(nn.Module ):



       def __init__(					self  ,UpperCAmelCase_   )	->				Dict:
           super().__init__()
           lowercase__									=    torchvision.models.resnetaaa(pretrained=UpperCAmelCase_   )
           lowercase__									=    list(model.children()   )[:-2]
           lowercase__									=    nn.Sequential(*UpperCAmelCase_   )
           lowercase__									=    nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )







       def 				_a   (					self  ,UpperCAmelCase_   )	->				str:
           # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
           lowercase__									=    self.pool(self.model(UpperCAmelCase_   )   )
           lowercase__									=    torch.flatten(UpperCAmelCase_  ,start_dim=2   )
           lowercase__									=    out.transpose(1  ,2   ).contiguous()
           return out  # BxNx2048





class 					snake_case			(UpperCamelCase ):



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Dict:
           lowercase__									=    [json.loads(UpperCAmelCase_   ) for l in open(UpperCAmelCase_   )]
           lowercase__									=    os.path.dirname(UpperCAmelCase_   )
           lowercase__									=    tokenizer
           lowercase__									=    labels
           lowercase__									=    len(UpperCAmelCase_   )
           lowercase__									=    max_seq_length

           lowercase__									=    transforms



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



       def __getitem__(					self  ,UpperCAmelCase_   )	->				Optional[int]:
           lowercase__									=    torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]  ,add_special_tokens=UpperCAmelCase_   )   )
           lowercase__							,							lowercase__							,							lowercase__									=    sentence[0], sentence[1:-1], sentence[-1]
           lowercase__									=    sentence[: self.max_seq_length]

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

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

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







       def 				_a   (					self   )	->				str:
           lowercase__									=    Counter()
           for row in self.data:
               label_freqs.update(row["label"]   )
           return label_freqs





def 							lowerCamelCase	(					_snake_case	:    Optional[int]							):





    '''simple docstring'''


    lowercase__									=    [len(row["sentence"]							) for row in batch]
    lowercase__							,							lowercase__									=    len(_snake_case							), max(_snake_case							)

    lowercase__									=    torch.zeros(_snake_case  ,_snake_case  ,dtype=torch.long							)
    lowercase__									=    torch.zeros(_snake_case  ,_snake_case  ,dtype=torch.long							)

    for i_batch, (input_row, length) in enumerate(zip(_snake_case  ,_snake_case							)							):
        lowercase__									=    input_row["sentence"]
        lowercase__									=    1

    lowercase__									=    torch.stack([row["image"] for row in batch]							)
    lowercase__									=    torch.stack([row["label"] for row in batch]							)
    lowercase__									=    torch.stack([row["image_start_token"] for row in batch]							)
    lowercase__									=    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 							lowerCamelCase	(					):





    '''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 							lowerCamelCase	(					):





    '''simple docstring'''


    return transforms.Compose(
        [
            transforms.Resize(256							),
            transforms.CenterCrop(224							),
            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]  ,),
        ]							)




