


'''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		={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							,							_lowerCamelCase):
										super().__init__()
										UpperCAmelCase__					:   Tuple       				=       torchvision.models.resnetaaa(pretrained=_lowerCamelCase)
										UpperCAmelCase__					:   List[str]       				=       list(model.children())[:-2]
										UpperCAmelCase__					:   Optional[int]       				=       nn.Sequential(*_lowerCamelCase)
										UpperCAmelCase__					:   str       				=       nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])




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


class 						_snake_case   (       a__		):




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

										UpperCAmelCase__					:   Dict       				=       transforms




							def __len__( self):
										return len(self.data)




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

										UpperCAmelCase__					:   List[Any]       				=       torch.zeros(self.n_classes)
										UpperCAmelCase__					:   List[str]       				=       1

										UpperCAmelCase__					:   Union[str, Any]       				=       Image.open(os.path.join(self.data_dir							,							self.data[index]["""img"""])).convert("""RGB""")
										UpperCAmelCase__					:   List[Any]       				=       self.transforms(_lowerCamelCase)

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




							def 			snake_case__  ( self):
										UpperCAmelCase__					:   Tuple       				=       Counter()
										for row in self.data:
													label_freqs.update(row["""label"""])
										return label_freqs







def       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__					:   Union[str, Any]       				=       [len(row["""sentence"""]   ) for row in batch]
			UpperCAmelCase__					:   Any       				=       len(UpperCamelCase__   ), max(UpperCamelCase__   )

			UpperCAmelCase__					:   str       				=       torch.zeros(UpperCamelCase__	, UpperCamelCase__	, dtype=torch.long   )
			UpperCAmelCase__					:   Union[str, Any]       				=       torch.zeros(UpperCamelCase__	, UpperCamelCase__	, dtype=torch.long   )

			for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase__	, UpperCamelCase__   )   ):
						UpperCAmelCase__					:   List[Any]       				=       input_row["""sentence"""]
						UpperCAmelCase__					:   List[Any]       				=       1

			UpperCAmelCase__					:   Union[str, Any]       				=       torch.stack([row["""image"""] for row in batch]   )
			UpperCAmelCase__					:   str       				=       torch.stack([row["""label"""] for row in batch]   )
			UpperCAmelCase__					:   str       				=       torch.stack([row["""image_start_token"""] for row in batch]   )
			UpperCAmelCase__					:   str       				=       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_5_6   ),
			        transforms.CenterCrop(2_2_4   ),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]	, std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]	, ),
			    ]   )



'''simple docstring'''



import os
from datetime import datetime as dt

from github import Github


__A		=[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]







def       _UpperCamelCase (						):
			UpperCAmelCase__					:   Union[str, Any]       				=       Github(os.environ["""GITHUB_TOKEN"""]   )
			UpperCAmelCase__					:   Any       				=       g.get_repo("""huggingface/diffusers"""   )
			UpperCAmelCase__					:   Optional[int]       				=       repo.get_issues(state="""open"""   )

			for issue in open_issues:
						UpperCAmelCase__					:   Any       				=       sorted(issue.get_comments()	, key=lambda UpperCamelCase__   : i.created_at	, reverse=UpperCamelCase__   )
						UpperCAmelCase__					:   List[Any]       				=       comments[0] if len(UpperCamelCase__   ) > 0 else None
						if (
						    last_comment is not None
						    and last_comment.user.login == "github-actions[bot]"
						    and (dt.utcnow() - issue.updated_at).days > 7
						    and (dt.utcnow() - issue.created_at).days >= 3_0
						    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
						):
									# Closes the issue after 7 days of inactivity since the Stalebot notification.
									issue.edit(state="""closed"""   )
						elif (
						    "stale" in issue.get_labels()
						    and last_comment is not None
						    and last_comment.user.login != "github-actions[bot]"
						):
									# Opens the issue if someone other than Stalebot commented.
									issue.edit(state="""open"""   )
									issue.remove_from_labels("""stale"""   )
						elif (
						    (dt.utcnow() - issue.updated_at).days > 2_3
						    and (dt.utcnow() - issue.created_at).days >= 3_0
						    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
						):
									# Post a Stalebot notification after 23 days of inactivity.
									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/diffusers/blob/main/CONTRIBUTING.md) """
									    """are likely to be ignored."""   )
									issue.add_to_labels("""stale"""   )


if __name__ == "__main__":
		main()