



"""simple docstring"""





def 	UpperCAmelCase     (		A	:						Tuple       ):

  '''simple docstring'''


  return [
      {
          0: [1, 2],
          1: [0, 2],
          2: [0, 1, 3, 5],
          3: [2, 4],
          4: [3],
          5: [2, 6, 8],
          6: [5, 7],
          7: [6, 8],
          8: [5, 7],
      },
      {
          0: [6],
          1: [9],
          2: [4, 5],
          3: [4],
          4: [2, 3],
          5: [2],
          6: [0, 7],
          7: [6],
          8: [],
          9: [1],
      },
      {
          0: [4],
          1: [6],
          2: [],
          3: [5, 6, 7],
          4: [0, 6],
          5: [3, 8, 9],
          6: [1, 3, 4, 7],
          7: [3, 6, 8, 9],
          8: [5, 7],
          9: [5, 7],
      },
      {
          0: [1, 3],
          1: [0, 2, 4],
          2: [1, 3, 4],
          3: [0, 2, 4],
          4: [1, 2, 3],
      },
  ][index]




def 	UpperCAmelCase     (		A	:						dict[int, list[int]]       ):

  '''simple docstring'''


  _UpperCAmelCase							 =		0
  _UpperCAmelCase							 =		len(A       )  # No of vertices in graph
  _UpperCAmelCase							 =		[0] * n
  _UpperCAmelCase							 =		[False] * n

  def dfs(A	:						Dict     ,				A	:						List[Any]     ,				A	:						Any     ,				A	:						Any       ):
    _UpperCAmelCase							 =		True
    _UpperCAmelCase							 =		id_
    id_ += 1
    for to in graph[at]:
      if to == parent:
        pass
      elif not visited[to]:
        dfs(A     ,				A     ,				A     ,				id_       )
        _UpperCAmelCase							 =		min(low[at]     ,				low[to]       )
        if id_ <= low[to]:
          bridges.append((at, to) if at < to else (to, at)       )
      else:
        # This edge is a back edge and cannot be a bridge
        _UpperCAmelCase							 =		min(low[at]     ,				low[to]       )

  _UpperCAmelCase							 =		[]
  for i in range(A       ):
    if not visited[i]:
      dfs(A     ,				-1     ,				A     ,				id_       )
  return bridges


if __name__ == "__main__":
     import doctest

     doctest.testmod()







"""simple docstring"""





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 			lowercase__			(      nn.Module    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case = 16	,       snake_case = 88	,       snake_case = None	,       snake_case = 1	,       snake_case = 0.0	,       snake_case = 32	,       snake_case = None	,       snake_case = False	,       snake_case = None	,       snake_case = None	,       snake_case = "geglu"	,       snake_case = None	,       )			->							str:
									super().__init__()
									_UpperCAmelCase							 =		nn.ModuleList(
									    [
									        TransformeraDModel(
									            num_attention_heads=snake_case	,       attention_head_dim=snake_case	,       in_channels=snake_case	,       num_layers=snake_case	,       dropout=snake_case	,       norm_num_groups=snake_case	,       cross_attention_dim=snake_case	,       attention_bias=snake_case	,       sample_size=snake_case	,       num_vector_embeds=snake_case	,       activation_fn=snake_case	,       num_embeds_ada_norm=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
									_UpperCAmelCase							 =		0.5

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






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case=None	,       snake_case=None	,       snake_case=None	,       snake_case = True	,       )			->							Any:
									_UpperCAmelCase							 =		hidden_states

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

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

									if not return_dict:
											return (output_states,)

									return TransformeraDModelOutput(sample=snake_case						)


