
"""simple docstring"""


from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin




@dataclass
class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      torch.FloatTensor




class  __lowerCamelCase  ( lowerCAmelCase						,	lowerCAmelCase			):




				@register_to_config
				def __init__(				self							,						UpperCAmelCase = 16							,						UpperCAmelCase = 88							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = 1							,						UpperCAmelCase = 0.0							,						UpperCAmelCase = 32							,						UpperCAmelCase = None							,						UpperCAmelCase = False							,						UpperCAmelCase = None							,						UpperCAmelCase = "geglu"							,						UpperCAmelCase = True							,						UpperCAmelCase = True							,						):
							super().__init__()
							lowerCamelCase_					=  num_attention_heads
							lowerCamelCase_					=  attention_head_dim
							lowerCamelCase_					=  num_attention_heads * attention_head_dim

							lowerCamelCase_					=  in_channels

							lowerCamelCase_					=  torch.nn.GroupNorm(num_groups=UpperCAmelCase							,						num_channels=UpperCAmelCase							,						eps=1e-6							,						affine=UpperCAmelCase   )
							lowerCamelCase_					=  nn.Linear(UpperCAmelCase							,						UpperCAmelCase   )

							# 3. Define transformers blocks
							lowerCamelCase_					=  nn.ModuleList(
							    [
							        BasicTransformerBlock(
							            UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						dropout=UpperCAmelCase							,						cross_attention_dim=UpperCAmelCase							,						activation_fn=UpperCAmelCase							,						attention_bias=UpperCAmelCase							,						double_self_attention=UpperCAmelCase							,						norm_elementwise_affine=UpperCAmelCase							,						)
							        for d in range(UpperCAmelCase   )
							    ]   )

							lowerCamelCase_					=  nn.Linear(UpperCAmelCase							,						UpperCAmelCase   )





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=1							,						UpperCAmelCase=None							,						UpperCAmelCase = True							,						):
							lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  hidden_states.shape
							lowerCamelCase_					=  batch_frames // num_frames

							lowerCamelCase_					=  hidden_states

							lowerCamelCase_					=  hidden_states[None, :].reshape(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
							lowerCamelCase_					=  hidden_states.permute(0							,						2							,						1							,						3							,						4   )

							lowerCamelCase_					=  self.norm(UpperCAmelCase   )
							lowerCamelCase_					=  hidden_states.permute(0							,						3							,						4							,						2							,						1   ).reshape(batch_size * height * width							,						UpperCAmelCase							,						UpperCAmelCase   )

							lowerCamelCase_					=  self.proj_in(UpperCAmelCase   )

							# 2. Blocks
							for block in self.transformer_blocks:
										lowerCamelCase_					=  block(
										    UpperCAmelCase							,						encoder_hidden_states=UpperCAmelCase							,						timestep=UpperCAmelCase							,						cross_attention_kwargs=UpperCAmelCase							,						class_labels=UpperCAmelCase							,						)

							# 3. Output
							lowerCamelCase_					=  self.proj_out(UpperCAmelCase   )
							lowerCamelCase_					=  (
							    hidden_states[None, None, :]
							    .reshape(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
							    .permute(0							,						3							,						4							,						1							,						2   )
							    .contiguous()
							)
							lowerCamelCase_					=  hidden_states.reshape(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )

							lowerCamelCase_					=  hidden_states + residual

							if not return_dict:
										return (output,)

							return TransformerTemporalModelOutput(sample=UpperCAmelCase   )





"""simple docstring"""


import os
from datetime import datetime as dt

from github import Github


A_    							=					[
    """good first issue""",
    """feature request""",
    """wip""",
]






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

			for issue in open_issues:
						lowerCamelCase_					=  sorted([comment for comment in issue.get_comments()]	,key=lambda lowerCAmelCase__						: i.created_at	,reverse=lowerCAmelCase__						)
						lowerCamelCase_					=  comments[0] if len(lowerCAmelCase__						) > 0 else None
						lowerCamelCase_					=  dt.utcnow()
						lowerCamelCase_					=  (current_time - issue.updated_at).days
						lowerCamelCase_					=  (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()



