



"""simple docstring"""




from collections import deque






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Union[str, Any]      )				->Optional[int]:






	'''simple docstring'''






	a   :	Any			=       len(A_      )
	a   :	str			=       deque()
	a   :	List[str]			=       [False for _ in range(A_      )]
	a   :	List[str]			=       [-1 for _ in range(A_      )]
	a   :	str			=       index_of[:]

	def strong_connect(_lowercase		:					Optional[int]					, _lowercase		:					List[str]					, _lowercase		:					Optional[int]      ):
		a   :	List[Any]			=       index  # the number when this node is seen
		a   :	Tuple			=       index  # lowest rank node reachable from here
		index += 1
		stack.append(A_      )
		a   :	Any			=       True

		for w in g[v]:
			if index_of[w] == -1:
				a   :	str			=       strong_connect(A_					, A_					, A_      )
				a   :	Any			=       (
				    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
				)
			elif on_stack[w]:
				a   :	int			=       (
				    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
				)

		if lowlink_of[v] == index_of[v]:
			a   :	Optional[int]			=       []
			a   :	Tuple			=       stack.pop()
			a   :	List[str]			=       False
			component.append(A_      )
			while w != v:
				a   :	str			=       stack.pop()
				a   :	List[Any]			=       False
				component.append(A_      )
			components.append(A_      )
		return index

	a   :	int			=       []
	for v in range(A_      ):
		if index_of[v] == -1:
			strong_connect(A_					, 0					, A_      )

	return components






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]					, _lowercase		:					str      )				->Tuple:






	'''simple docstring'''






	a   :	str			=       [[] for _ in range(A_      )]
	for u, v in edges:
		g[u].append(A_      )
	return g


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

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









"""simple docstring"""




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


class  __UpperCamelCase       (		nn.Module		):
							lowerCamelCase     :      int
							lowerCamelCase     :      jnp.dtype         =jnp.floataa






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


class  __UpperCamelCase       (		nn.Module		):
							lowerCamelCase     :      int
							lowerCamelCase     :      jnp.dtype         =jnp.floataa






							def 					__a     (     self   )			->				Dict:
								a   :	Optional[Any]			=       nn.Conv(
								    self.out_channels   ,      kernel_size=(3, 3)   ,      strides=(2, 2)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )






							def __call__(     self   ,      lowerCAmelCase__   )			->				Tuple:
								# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
								# hidden_states = jnp.pad(hidden_states, pad_width=pad)
								a   :	Tuple			=       self.conv(lowerCAmelCase__   )
								return hidden_states


class  __UpperCamelCase       (		nn.Module		):
							lowerCamelCase     :      int
							lowerCamelCase     :      int         =None
							lowerCamelCase     :      float         =0.0
							lowerCamelCase     :      bool         =None
							lowerCamelCase     :      jnp.dtype         =jnp.floataa






							def 					__a     (     self   )			->				int:
								a   :	Dict			=       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   :	List[Any]			=       nn.Conv(
								    lowerCAmelCase__   ,      kernel_size=(3, 3)   ,      strides=(1, 1)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )

								a   :	List[Any]			=       nn.Dense(lowerCAmelCase__   ,      dtype=self.dtype   )

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

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

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






							def __call__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__=True   )			->				str:
								a   :	int			=       hidden_states
								a   :	Tuple			=       self.norma(lowerCAmelCase__   )
								a   :	Any			=       nn.swish(lowerCAmelCase__   )
								a   :	int			=       self.conva(lowerCAmelCase__   )

								a   :	int			=       self.time_emb_proj(nn.swish(lowerCAmelCase__   )   )
								a   :	Tuple			=       jnp.expand_dims(jnp.expand_dims(lowerCAmelCase__   ,      1   )   ,      1   )
								a   :	Dict			=       hidden_states + temb

								a   :	str			=       self.norma(lowerCAmelCase__   )
								a   :	List[Any]			=       nn.swish(lowerCAmelCase__   )
								a   :	List[str]			=       self.dropout(lowerCAmelCase__   ,      lowerCAmelCase__   )
								a   :	List[str]			=       self.conva(lowerCAmelCase__   )

								if self.conv_shortcut is not None:
									a   :	Tuple			=       self.conv_shortcut(lowerCAmelCase__   )

								return hidden_states + residual


