



'''simple docstring'''






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

class 							a__(      nn.Module			):
    lowercase__				=					42
    lowercase__				=					jnp.floataa







    def     lowercase_ (   self				:						int			):
      a		:      str       =  nn.Conv(
          self.out_channels		,    kernel_size=(3, 3)		,    strides=(1, 1)		,    padding=((1, 1), (1, 1))		,    dtype=self.dtype		,    )







    def __call__(   self				:						Dict		,    __snake_case				:						Union[str, Any]			):
      a     ,			a     ,			a     ,			a		:      str       =  hidden_states.shape
      a		:      int       =  jax.image.resize(
          __snake_case		,    shape=(batch, height * 2, width * 2, channels)		,    method='nearest'		,    )
      a		:      Optional[Any]       =  self.conv(__snake_case			)
      return hidden_states

class 							a__(      nn.Module			):
    lowercase__				=					42
    lowercase__				=					jnp.floataa







    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      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				:						Union[str, Any]		,    __snake_case				:						Any			):
      # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
      # hidden_states = jnp.pad(hidden_states, pad_width=pad)
      a		:      Optional[Any]       =  self.conv(__snake_case			)
      return hidden_states

class 							a__(      nn.Module			):
    lowercase__				=					42
    lowercase__				=					None
    lowercase__				=					0.0
    lowercase__				=					None
    lowercase__				=					jnp.floataa







    def     lowercase_ (   self				:						List[Any]			):
      a		:      List[Any]       =  self.in_channels if self.out_channels is None else self.out_channels

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

      a		:      Any       =  nn.Dense(__snake_case		,    dtype=self.dtype			)

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

      a		:      List[Any]       =  self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

      a		:      Tuple       =  None
      if use_nin_shortcut:
        a		:      str       =  nn.Conv(
            __snake_case		,    kernel_size=(1, 1)		,    strides=(1, 1)		,    padding='VALID'		,    dtype=self.dtype		,    )







    def __call__(   self				:						Any		,    __snake_case				:						str		,    __snake_case				:						Tuple		,    __snake_case				:						List[str]=True			):
      a		:      Tuple       =  hidden_states
      a		:      Optional[int]       =  self.norma(__snake_case			)
      a		:      List[str]       =  nn.swish(__snake_case			)
      a		:      Any       =  self.conva(__snake_case			)

      a		:      Any       =  self.time_emb_proj(nn.swish(__snake_case			)			)
      a		:      Optional[int]       =  jnp.expand_dims(jnp.expand_dims(__snake_case		,    1			)		,    1			)
      a		:      Tuple       =  hidden_states + temb

      a		:      Tuple       =  self.norma(__snake_case			)
      a		:      Optional[Any]       =  nn.swish(__snake_case			)
      a		:      List[Any]       =  self.dropout(__snake_case		,    __snake_case			)
      a		:      Dict       =  self.conva(__snake_case			)

      if self.conv_shortcut is not None:
        a		:      int       =  self.conv_shortcut(__snake_case			)

      return hidden_states + residual




'''simple docstring'''






class 							a__:







    def __init__(   self				:						Dict		,    __snake_case				:						Optional[int]		,    __snake_case				:						Any		,    __snake_case				:						Tuple			):
      a		:      List[str]       =  name
      a		:      Dict       =  value
      a		:      List[str]       =  weight







    def __repr__(   self				:						int			):
      return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""







    def     lowercase_ (   self				:						Optional[int]			):
      return self.value







    def     lowercase_ (   self				:						List[str]			):
      return self.name







    def     lowercase_ (   self				:						int			):
      return self.weight







    def     lowercase_ (   self				:						List[str]			):
      return self.value / self.weight
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[int]       =  []
  for i in range(len(_A       )       ):
    menu.append(Things(name[i]				,    value[i]				,    weight[i]       )       )
  return menu
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[Any]       =  sorted(_A				,    key=_A				,    reverse=_A       )
  a		:      Optional[int]       =  []
  a     ,			a		:      str       =  0.0, 0.0
  for i in range(len(_A       )       ):
    if (total_cost + items_copy[i].get_weight()) <= max_cost:
      result.append(items_copy[i]       )
      total_cost += items_copy[i].get_weight()
      total_value += items_copy[i].get_value()
  return (result, total_value)
def    lowerCamelCase__ (		):
  pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()