



'''simple docstring'''





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput






class __A    (						nn.Module		):



 '''simple docstring'''







 def __init__(self				,     A = 16				,     A = 88				,     A = None				,     A = 1				,     A = 0.0				,     A = 32				,     A = None				,     A = False				,     A = None				,     A = None				,     A = "geglu"				,     A = None				,     )					->  Union[str, Any]:


      """simple docstring"""



      super().__init__()
      _a											=						nn.ModuleList(
          [
              TransformeraDModel(
                  num_attention_heads=A				,     attention_head_dim=A				,     in_channels=A				,     num_layers=A				,     dropout=A				,     norm_num_groups=A				,     cross_attention_dim=A				,     attention_bias=A				,     sample_size=A				,     num_vector_embeds=A				,     activation_fn=A				,     num_embeds_ada_norm=A				,     )
              for _ in range(2 )
          ] )

      # Variables that can be set by a pipeline:

      # The ratio of transformer1 to transformer2's output states to be combined during inference
      _a											=						0.5

      # The shape of `encoder_hidden_states` is expected to be
      # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
      _a											=						[77, 257]

      # Which transformer to use to encode which condition.
      # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
      _a											=						[1, 0]



 def       a__						(self				,     A				,     A				,     A=None				,     A=None				,     A=None				,     A = True				,     )					->  List[str]:


      """simple docstring"""



      _a											=						hidden_states

      _a											=						[]
      _a											=						0
      # attention_mask is not used yet
      for i in range(2 ):
           # for each of the two transformers, pass the corresponding condition tokens
           _a											=						encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
           _a											=						self.transformer_index_for_condition[i]
           _a											=						self.transformers[transformer_index](
               A				,     encoder_hidden_states=A				,     timestep=A				,     cross_attention_kwargs=A				,     return_dict=A				,     )[0]
           encoded_states.append(encoded_state - input_states )
           tokens_start += self.condition_lengths[i]

      _a											=						encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
      _a											=						output_states + input_states

      if not return_dict:
           return (output_states,)

      return TransformeraDModelOutput(sample=A )








'''simple docstring'''





def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						set()
     # To detect a back edge, keep track of vertices currently in the recursion stack
     _a											=						set()
     return any(
         node not in visited and depth_first_search(__A						,			__A						,			__A						,			__A)
         for node in graph)




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A):



     """simple docstring"""



     visited.add(__A)
     rec_stk.add(__A)

     for node in graph[vertex]:
          if node not in visited:
               if depth_first_search(__A						,			__A						,			__A						,			__A):
                    return True
          elif node in rec_stk:
               return True

    # The node needs to be removed from recursion stack before function ends
     rec_stk.remove(__A)
     return False


if __name__ == "__main__":
  from doctest import testmod

  testmod()



