



'''simple docstring'''





import argparse
from collections import defaultdict




def 							lowerCamelCase	(					_snake_case	:    Union[str, Any]  ,_snake_case	:    Dict  ,_snake_case	:    Optional[Any]  ,_snake_case	:    List[Any]  ,_snake_case	:    Union[str, Any]							):





    '''simple docstring'''


    lowercase__									=    f'''{file}_{class_name}_{test_name}'''
    done_test[_id] += 1

    with open(_snake_case  ,"r"							) as f:
        lowercase__									=    f.readlines()

    lowercase__									=    f'''class {class_name}('''
    lowercase__									=    f'''{4 * ' '}def {test_name}('''
    lowercase__									=    f'''{8 * ' '}{correct_line.split()[0]}'''
    lowercase__									=    f'''{16 * ' '}{correct_line.split()[0]}'''
    lowercase__									=    False
    lowercase__									=    False
    lowercase__									=    False
    lowercase__									=    False
    lowercase__									=    0
    lowercase__									=    0

    lowercase__									=    []
    for line in lines:
        if line.startswith(_snake_case							):
            lowercase__									=    True
        elif in_class and line.startswith(_snake_case							):
            lowercase__									=    True
        elif in_class and in_func and (line.startswith(_snake_case							) or line.startswith(_snake_case							)):
            lowercase__									=    len(line.split(correct_line.split()[0]							)[0]							)
            count += 1

            if count == done_test[_id]:
                lowercase__									=    True

        if in_class and in_func and in_line:
            if ")" not in line:
                continue
            else:
                lowercase__									=    True

        if in_class and in_func and in_line and insert_line:
            new_lines.append(f'''{spaces * ' '}{correct_line}'''							)
            lowercase__									=    lowercase__									=    lowercase__									=    lowercase__									=    False
        else:
            new_lines.append(_snake_case							)

    with open(_snake_case  ,"w"							) as f:
        for line in new_lines:
            f.write(_snake_case							)




def 							lowerCamelCase	(					_snake_case	:    Optional[int]  ,_snake_case	:    str=None							):





    '''simple docstring'''


    if fail is not None:
        with open(_snake_case  ,"r"							) as f:
            lowercase__									=    {l.strip() for l in f.readlines()}
    else:
        lowercase__									=    None

    with open(_snake_case  ,"r"							) as f:
        lowercase__									=    f.readlines()

    lowercase__									=    defaultdict(_snake_case							)
    for line in correct_lines:
        lowercase__							,							lowercase__							,							lowercase__							,							lowercase__									=    line.split(";"							)
        if test_failures is None or "::".join([file, class_name, test_name]							) in test_failures:
            overwrite_file(_snake_case  ,_snake_case  ,_snake_case  ,_snake_case  ,_snake_case							)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__          =  argparse.ArgumentParser()
 parser.add_argument("--correct_filename", help="filename of tests with expected result")
 parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
 SCREAMING_SNAKE_CASE__          =  parser.parse_args()

 main(args.correct_filename, args.fail_filename)









'''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]  ,),
        ]							)




