




'''simple docstring'''



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput





class      lowerCAmelCase (			nn.Module):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE = 16     ,	__SCREAMING_SNAKE_CASE = 88     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = 1     ,	__SCREAMING_SNAKE_CASE = 0.0     ,	__SCREAMING_SNAKE_CASE = 32     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = False     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = "geglu"     ,	__SCREAMING_SNAKE_CASE = None     ,	)   ->	Optional[int]:



            '''simple docstring'''




            super().__init__()
            __snake_case            =	nn.ModuleList(
                [
                    TransformeraDModel(
                        num_attention_heads=__SCREAMING_SNAKE_CASE     ,	attention_head_dim=__SCREAMING_SNAKE_CASE     ,	in_channels=__SCREAMING_SNAKE_CASE     ,	num_layers=__SCREAMING_SNAKE_CASE     ,	dropout=__SCREAMING_SNAKE_CASE     ,	norm_num_groups=__SCREAMING_SNAKE_CASE     ,	cross_attention_dim=__SCREAMING_SNAKE_CASE     ,	attention_bias=__SCREAMING_SNAKE_CASE     ,	sample_size=__SCREAMING_SNAKE_CASE     ,	num_vector_embeds=__SCREAMING_SNAKE_CASE     ,	activation_fn=__SCREAMING_SNAKE_CASE     ,	num_embeds_ada_norm=__SCREAMING_SNAKE_CASE     ,	)
                    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
            __snake_case            =	0.5

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




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE = True     ,	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	hidden_states

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

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

            if not return_dict:
                 return (output_states,)

            return TransformeraDModelOutput(sample=__SCREAMING_SNAKE_CASE	)












'''simple docstring'''



from collections import deque







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any]						)-> Optional[int]:




     '''simple docstring'''





     __snake_case            =	len(_lowerCamelCase						)
     __snake_case            =	deque()
     __snake_case            =	[False for _ in range(_lowerCamelCase						)]
     __snake_case            =	[-1 for _ in range(_lowerCamelCase						)]
     __snake_case            =	index_of[:]

     def strong_connect(_lowerCamelCase				:  Any ,						_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  List[str]						):
          __snake_case            =	index  # the number when this node is seen
          __snake_case            =	index  # lowest rank node reachable from here
          index += 1
          stack.append(_lowerCamelCase						)
          __snake_case            =	True

          for w in g[v]:
               if index_of[w] == -1:
                    __snake_case            =	strong_connect(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)
                    __snake_case            =	(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )
               elif on_stack[w]:
                    __snake_case            =	(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )

          if lowlink_of[v] == index_of[v]:
               __snake_case            =	[]
               __snake_case            =	stack.pop()
               __snake_case            =	False
               component.append(_lowerCamelCase						)
               while w != v:
                    __snake_case            =	stack.pop()
                    __snake_case            =	False
                    component.append(_lowerCamelCase						)
               components.append(_lowerCamelCase						)
          return index

     __snake_case            =	[]
     for v in range(_lowerCamelCase						):
          if index_of[v] == -1:
               strong_connect(_lowerCamelCase ,						0 ,						_lowerCamelCase						)

     return components







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  Optional[Any]						)-> Dict:




     '''simple docstring'''





     __snake_case            =	[[] for _ in range(_lowerCamelCase						)]
     for u, v in edges:
          g[u].append(_lowerCamelCase						)
     return g


if __name__ == "__main__":
     # Test
     UpperCAmelCase_							:	List[str]	     =		7
     UpperCAmelCase_							:	int	     =		[0, 0, 1, 2, 3, 3, 4, 4, 6]
     UpperCAmelCase_							:	Dict	     =		[1, 3, 2, 0, 1, 4, 5, 6, 5]
     UpperCAmelCase_							:	List[str]	     =		[(u, v) for u, v in zip(source, target)]
     UpperCAmelCase_							:	Tuple	     =		create_graph(n_vertices, edges)

     assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)






