


import os
from datetime import datetime as dt

from github import Github


_A			=						[
    'good first issue',
    'feature request',
    'wip',
]

def 						_UpperCAmelCase    (      ):
     __UpperCamelCase   							=Github(os.environ['GITHUB_TOKEN']    )
     __UpperCamelCase   							=g.get_repo('huggingface/accelerate'    )
     __UpperCamelCase   							=repo.get_issues(state='open'    )

     for issue in open_issues:
          __UpperCamelCase   							=sorted([comment for comment in issue.get_comments()]      ,					key=lambda SCREAMING_SNAKE_CASE__    : i.created_at      ,					reverse=SCREAMING_SNAKE_CASE__    )
          __UpperCamelCase   							=comments[0] if len(SCREAMING_SNAKE_CASE__    ) > 0 else None
          __UpperCamelCase   							=dt.utcnow()
          __UpperCamelCase   							=(current_time - issue.updated_at).days
          __UpperCamelCase   							=(current_time - issue.created_at).days
          if (
              last_comment is not None
              and last_comment.user.login == "github-actions[bot]"
              and days_since_updated > 7
              and days_since_creation >= 30
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
          ):
               # Close issue since it has been 7 days of inactivity since bot mention.
               issue.edit(state='closed'    )
          elif (
              days_since_updated > 23
              and days_since_creation >= 30
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
          ):
               # Add stale comment
               issue.create_comment(
                   'This issue has been automatically marked as stale because it has not had '
                   'recent activity. If you think this still needs to be addressed '
                   'please comment on this thread.\n\nPlease note that issues that do not follow the '
                   '[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '
                   'are likely to be ignored.'    )


if __name__ == "__main__":
   main()









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			=						{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_   )  ->	Optional[int]:
         super().__init__()
         __UpperCamelCase   							=torchvision.models.resnetaaa(pretrained=A_   )
         __UpperCamelCase   							=list(model.children()   )[:-2]
         __UpperCamelCase   							=nn.Sequential(*A_   )
         __UpperCamelCase   							=nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )






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






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ ,       A_ ,       A_ ,       A_   )  ->	List[str]:
         __UpperCamelCase   							=[json.loads(A_   ) for l in open(A_   )]
         __UpperCamelCase   							=os.path.dirname(A_   )
         __UpperCamelCase   							=tokenizer
         __UpperCamelCase   							=labels
         __UpperCamelCase   							=len(A_   )
         __UpperCamelCase   							=max_seq_length

         __UpperCamelCase   							=transforms






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






    def __getitem__(      self ,       A_   )  ->	Union[str, Any]:
         __UpperCamelCase   							=torch.LongTensor(self.tokenizer.encode(self.data[index]['text'] ,       add_special_tokens=A_   )   )
         __UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=sentence[0], sentence[1:-1], sentence[-1]
         __UpperCamelCase   							=sentence[: self.max_seq_length]

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

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

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






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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     __UpperCamelCase   							=[len(row['sentence']    ) for row in batch]
     __UpperCamelCase							,				__UpperCamelCase   							=len(SCREAMING_SNAKE_CASE__    ), max(SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase   							=torch.zeros(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					dtype=torch.long    )
     __UpperCamelCase   							=torch.zeros(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					dtype=torch.long    )

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

     __UpperCamelCase   							=torch.stack([row['image'] for row in batch]    )
     __UpperCamelCase   							=torch.stack([row['label'] for row in batch]    )
     __UpperCamelCase   							=torch.stack([row['image_start_token'] for row in batch]    )
     __UpperCamelCase   							=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 						_UpperCAmelCase    (      ):
     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 						_UpperCAmelCase    (      ):
     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]      ,					),
         ]    )





