




import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__A								=       logging.get_logger(__name__)  # pylint: disable=invalid-name







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__=768				)       -> Optional[int]:



							'''simple docstring'''
							super().__init__(lowerCamelCase__				)
							__lowerCamelCase									=       proj_size

							__lowerCamelCase									=       CLIPVisionModel(lowerCamelCase__				)
							__lowerCamelCase									=       PaintByExampleMapper(lowerCamelCase__				)
							__lowerCamelCase									=       nn.LayerNorm(config.hidden_size				)
							__lowerCamelCase									=       nn.Linear(config.hidden_size     ,		self.proj_size				)

							# uncondition for scaling
							__lowerCamelCase									=       nn.Parameter(torch.randn((1, 1, self.proj_size)				)				)






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__=False				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       self.model(pixel_values=lowerCamelCase__				)
							__lowerCamelCase									=       clip_output.pooler_output
							__lowerCamelCase									=       self.mapper(latent_states[:, None]				)
							__lowerCamelCase									=       self.final_layer_norm(lowerCamelCase__				)
							__lowerCamelCase									=       self.proj_out(lowerCamelCase__				)
							if return_uncond_vector:
											return latent_states, self.uncond_vector

							return latent_states







class        __lowerCAmelCase					(			nn.Module					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							super().__init__()
							__lowerCamelCase									=       (config.num_hidden_layers + 1) // 5
							__lowerCamelCase									=       config.hidden_size
							__lowerCamelCase									=       1
							__lowerCamelCase									=       nn.ModuleList(
							    [
							        BasicTransformerBlock(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		activation_fn='gelu'     ,		attention_bias=lowerCamelCase__				)
							        for _ in range(lowerCamelCase__				)
							    ]				)






			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> int:



							'''simple docstring'''
							for block in self.blocks:
											__lowerCamelCase									=       block(lowerCamelCase__				)

							return hidden_states








from datetime import datetime as dt
import os

from github import Github


__A								=       [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]







def      lowerCamelCase_						(			)	->			int:





				"""simple docstring"""


				__lowerCamelCase									=       Github(os.environ['GITHUB_TOKEN'] )
				__lowerCamelCase									=       g.get_repo('huggingface/transformers' )
				__lowerCamelCase									=       repo.get_issues(state='open' )

				for issue in open_issues:
								__lowerCamelCase									=       sorted([comment for comment in issue.get_comments()]					,	key=lambda UpperCamelCase__ : i.created_at					,	reverse=UpperCamelCase__ )
								__lowerCamelCase									=       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 >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
								):
												# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
												issue.edit(state='closed' )
								elif (
								    (dt.utcnow() - issue.updated_at).days > 23
								    and (dt.utcnow() - issue.created_at).days >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
								):
												# print(f"Would add stale comment to {issue.number}")
												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/transformers/blob/main/CONTRIBUTING.md) '
												    'are likely to be ignored.' )


if __name__ == "__main__":
					main()


