


'''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 							__UpperCamelCase    (		lowerCAmelCase_ ):
 A_           =   42





class 							__UpperCamelCase    (		lowerCAmelCase_		,				lowerCAmelCase_ ):




 @register_to_config
 def __init__(       self						,    __a = 16						,    __a = 88						,    __a = None						,    __a = None						,    __a = 1						,    __a = 0.0						,    __a = 32						,    __a = None						,    __a = False						,    __a = None						,    __a = "geglu"						,    __a = True						,    __a = True						,    ):

      '''simple docstring'''





      super().__init__()
      __a      :	List[Any]    =  num_attention_heads
      __a      :	List[str]    =  attention_head_dim
      __a      :	Optional[Any]    =  num_attention_heads * attention_head_dim

      __a      :	Dict    =  in_channels

      __a      :	Dict    =  torch.nn.GroupNorm(num_groups=__a						,    num_channels=__a						,    eps=1E-6						,    affine=__a							)
      __a      :	Optional[int]    =  nn.Linear(__a						,    __a							)

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

      __a      :	Union[str, Any]    =  nn.Linear(__a						,    __a							)





 def 				__UpperCAmelCase							(       self						,    __a						,    __a=None						,    __a=None						,    __a=None						,    __a=1						,    __a=None						,    __a = True						,    ):

      '''simple docstring'''





      __a      :	Tuple    =  hidden_states.shape
      __a      :	int    =  batch_frames // num_frames

      __a      :	int    =  hidden_states

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

      __a      :	List[Any]    =  self.norm(__a							)
      __a      :	Any    =  hidden_states.permute(0						,    3						,    4						,    2						,    1							).reshape(batch_size * height * width						,    __a						,    __a							)

      __a      :	Any    =  self.proj_in(__a							)

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

      # 3. Output
      __a      :	Union[str, Any]    =  self.proj_out(__a							)
      __a      :	Any    =  (
          hidden_states[None, None, :]
          .reshape(__a						,    __a						,    __a						,    __a						,    __a							)
          .permute(0						,    3						,    4						,    1						,    2							)
          .contiguous()
      )
      __a      :	Union[str, Any]    =  hidden_states.reshape(__a						,    __a						,    __a						,    __a							)

      __a      :	Optional[Any]    =  hidden_states + residual

      if not return_dict:
           return (output,)

      return TransformerTemporalModelOutput(sample=__a							)







'''simple docstring'''



__lowercase     :						Optional[Any]	=     {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__lowercase     :						List[str]	=     ['a', 'b', 'c', 'd', 'e']






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       Any						,				_SCREAMING_SNAKE_CASE				:       List[str]       ):
					__a      :	Any    =  start
					# add current to visited
					visited.append(_SCREAMING_SNAKE_CASE       )
					__a      :	Union[str, Any]    =  edges[current]
					for neighbor in neighbors:
										# if neighbor not in visited, visit
										if neighbor not in visited:
															__a      :	Dict    =  topological_sort(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
    # if all neighbors visited add current to sort
					sort.append(_SCREAMING_SNAKE_CASE       )
					# if all vertices haven't been visited select a new one to visit
					if len(_SCREAMING_SNAKE_CASE       ) != len(_SCREAMING_SNAKE_CASE       ):
										for vertice in vertices:
															if vertice not in visited:
																				__a      :	List[Any]    =  topological_sort(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
    # return sort
					return sort


if __name__ == "__main__":
	__lowercase     :						Union[str, Any]	=     topological_sort('a', [], [])
	print(sort)



