





"""simple docstring"""


import flax.linen as nn
import jax
import jax.numpy as jnp



class      __SCREAMING_SNAKE_CASE    (						nn.Module   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:int
  SCREAMING_SNAKE_CASE__							:jnp.dtype	     =					jnp.floataa

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Any:
        _UpperCamelCase      :	str						=       nn.Conv(
            self.out_channels					,   kernel_size=(3, 3)					,   strides=(1, 1)					,   padding=((1, 1), (1, 1))					,   dtype=self.dtype					,   )


  def __call__(		self   :       List[str]					,   __a   :       int	)		->				List[Any]:
        _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	List[str]						=       hidden_states.shape
        _UpperCamelCase      :	Dict						=       jax.image.resize(
            __a					,   shape=(batch, height * 2, width * 2, channels)					,   method="nearest"					,   )
        _UpperCamelCase      :	Tuple						=       self.conv(__a	)
        return hidden_states





class      __SCREAMING_SNAKE_CASE    (						nn.Module   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:int
  SCREAMING_SNAKE_CASE__							:jnp.dtype	     =					jnp.floataa

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Any:
        _UpperCamelCase      :	List[Any]						=       nn.Conv(
            self.out_channels					,   kernel_size=(3, 3)					,   strides=(2, 2)					,   padding=((1, 1), (1, 1))					,   dtype=self.dtype					,   )


  def __call__(		self   :       List[Any]					,   __a   :       Any	)		->				Any:
        # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
        # hidden_states = jnp.pad(hidden_states, pad_width=pad)
        _UpperCamelCase      :	Union[str, Any]						=       self.conv(__a	)
        return hidden_states





class      __SCREAMING_SNAKE_CASE    (						nn.Module   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:int
  SCREAMING_SNAKE_CASE__							:int	     =					None
  SCREAMING_SNAKE_CASE__							:float	     =					0.0
  SCREAMING_SNAKE_CASE__							:bool	     =					None
  SCREAMING_SNAKE_CASE__							:jnp.dtype	     =					jnp.floataa

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				int:
        _UpperCamelCase      :	List[str]						=       self.in_channels if self.out_channels is None else self.out_channels

        _UpperCamelCase      :	int						=       nn.GroupNorm(num_groups=32					,   epsilon=1e-5	)
        _UpperCamelCase      :	List[str]						=       nn.Conv(
            __a					,   kernel_size=(3, 3)					,   strides=(1, 1)					,   padding=((1, 1), (1, 1))					,   dtype=self.dtype					,   )

        _UpperCamelCase      :	Union[str, Any]						=       nn.Dense(__a					,   dtype=self.dtype	)

        _UpperCamelCase      :	Union[str, Any]						=       nn.GroupNorm(num_groups=32					,   epsilon=1e-5	)
        _UpperCamelCase      :	Dict						=       nn.Dropout(self.dropout_prob	)
        _UpperCamelCase      :	List[str]						=       nn.Conv(
            __a					,   kernel_size=(3, 3)					,   strides=(1, 1)					,   padding=((1, 1), (1, 1))					,   dtype=self.dtype					,   )

        _UpperCamelCase      :	Optional[Any]						=       self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

        _UpperCamelCase      :	List[str]						=       None
        if use_nin_shortcut:
              _UpperCamelCase      :	List[Any]						=       nn.Conv(
                  __a					,   kernel_size=(1, 1)					,   strides=(1, 1)					,   padding="VALID"					,   dtype=self.dtype					,   )


  def __call__(		self   :       Optional[Any]					,   __a   :       Optional[int]					,   __a   :       Optional[int]					,   __a   :       Any=True	)		->				Dict:
        _UpperCamelCase      :	Optional[int]						=       hidden_states
        _UpperCamelCase      :	List[Any]						=       self.norma(__a	)
        _UpperCamelCase      :	Any						=       nn.swish(__a	)
        _UpperCamelCase      :	str						=       self.conva(__a	)

        _UpperCamelCase      :	Any						=       self.time_emb_proj(nn.swish(__a	)	)
        _UpperCamelCase      :	Any						=       jnp.expand_dims(jnp.expand_dims(__a					,   1	)					,   1	)
        _UpperCamelCase      :	str						=       hidden_states + temb

        _UpperCamelCase      :	Any						=       self.norma(__a	)
        _UpperCamelCase      :	Optional[Any]						=       nn.swish(__a	)
        _UpperCamelCase      :	Optional[Any]						=       self.dropout(__a					,   __a	)
        _UpperCamelCase      :	List[str]						=       self.conva(__a	)

        if self.conv_shortcut is not None:
              _UpperCamelCase      :	str						=       self.conv_shortcut(__a	)

        return hidden_states + residual







"""simple docstring"""


def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       set()
      # edges = list of graph's edges
      _UpperCamelCase      :	Union[str, Any]						=       get_edges(lowercase_			)

      # While there are still elements in edges list, take an arbitrary edge
      # (from_node, to_node) and add his extremity to chosen_vertices and then
      # remove all arcs adjacent to the from_node and to_node
      while edges:
            _UpperCamelCase,  _UpperCamelCase      :	str						=       edges.pop()
            chosen_vertices.add(lowercase_			)
            chosen_vertices.add(lowercase_			)
            for edge in edges.copy():
                  if from_node in edge or to_node in edge:
                        edges.discard(lowercase_			)
      return chosen_vertices




def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       set()
      for from_node, to_nodes in graph.items():
            for to_node in to_nodes:
                  edges.add((from_node, to_node)			)
      return edges


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
