




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

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD







class 				lowerCAmelCase_       (					nn.Module							):
				__a     :       int
				__a     :       int
				__a     :       float						=     0.0
				__a     :       int						=     1
				__a     :       int						=     1
				__a     :       bool						=     True
				__a     :       bool						=     False
				__a     :       bool						=     False
				__a     :       bool						=     False
				__a     :       jnp.dtype						=     jnp.floataa






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      []
							SCREAMING_SNAKE_CASE_   : Dict					  =      []

							for i in range(self.num_layers ):
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.in_channels if i == 0 else self.out_channels

										SCREAMING_SNAKE_CASE_   : str					  =      FlaxResnetBlockaD(
										    in_channels=snake_case__      ,out_channels=self.out_channels      ,dropout_prob=self.dropout      ,dtype=self.dtype      ,)
										resnets.append(snake_case__ )

										SCREAMING_SNAKE_CASE_   : List[Any]					  =      FlaxTransformeraDModel(
										    in_channels=self.out_channels      ,n_heads=self.num_attention_heads      ,d_head=self.out_channels // self.num_attention_heads      ,depth=1      ,use_linear_projection=self.use_linear_projection      ,only_cross_attention=self.only_cross_attention      ,use_memory_efficient_attention=self.use_memory_efficient_attention      ,dtype=self.dtype      ,)
										attentions.append(snake_case__ )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      resnets
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      attentions

							if self.add_downsample:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      FlaxDownsampleaD(self.out_channels      ,dtype=self.dtype )






				def __call__(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__=True ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      ()

							for resnet, attn in zip(self.resnets      ,self.attentions ):
										SCREAMING_SNAKE_CASE_   : Tuple					  =      resnet(snake_case__      ,snake_case__      ,deterministic=snake_case__ )
										SCREAMING_SNAKE_CASE_   : str					  =      attn(snake_case__      ,snake_case__      ,deterministic=snake_case__ )
										output_states += (hidden_states,)

							if self.add_downsample:
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.downsamplers_a(snake_case__ )
										output_states += (hidden_states,)

							return hidden_states, output_states







class 				lowerCAmelCase_       (					nn.Module							):
				__a     :       int
				__a     :       int
				__a     :       float						=     0.0
				__a     :       int						=     1
				__a     :       bool						=     True
				__a     :       jnp.dtype						=     jnp.floataa






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      []

							for i in range(self.num_layers ):
										SCREAMING_SNAKE_CASE_   : List[str]					  =      self.in_channels if i == 0 else self.out_channels

										SCREAMING_SNAKE_CASE_   : List[str]					  =      FlaxResnetBlockaD(
										    in_channels=snake_case__      ,out_channels=self.out_channels      ,dropout_prob=self.dropout      ,dtype=self.dtype      ,)
										resnets.append(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Dict					  =      resnets

							if self.add_downsample:
										SCREAMING_SNAKE_CASE_   : Any					  =      FlaxDownsampleaD(self.out_channels      ,dtype=self.dtype )






				def __call__(			self      ,snake_case__      ,snake_case__      ,snake_case__=True ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      ()

							for resnet in self.resnets:
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      resnet(snake_case__      ,snake_case__      ,deterministic=snake_case__ )
										output_states += (hidden_states,)

							if self.add_downsample:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      self.downsamplers_a(snake_case__ )
										output_states += (hidden_states,)

							return hidden_states, output_states







class 				lowerCAmelCase_       (					nn.Module							):
				__a     :       int
				__a     :       int
				__a     :       int
				__a     :       float						=     0.0
				__a     :       int						=     1
				__a     :       int						=     1
				__a     :       bool						=     True
				__a     :       bool						=     False
				__a     :       bool						=     False
				__a     :       bool						=     False
				__a     :       jnp.dtype						=     jnp.floataa






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Any					  =      []
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      []

							for i in range(self.num_layers ):
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.in_channels if (i == self.num_layers - 1) else self.out_channels
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.prev_output_channel if i == 0 else self.out_channels

										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      FlaxResnetBlockaD(
										    in_channels=resnet_in_channels + res_skip_channels      ,out_channels=self.out_channels      ,dropout_prob=self.dropout      ,dtype=self.dtype      ,)
										resnets.append(snake_case__ )

										SCREAMING_SNAKE_CASE_   : List[Any]					  =      FlaxTransformeraDModel(
										    in_channels=self.out_channels      ,n_heads=self.num_attention_heads      ,d_head=self.out_channels // self.num_attention_heads      ,depth=1      ,use_linear_projection=self.use_linear_projection      ,only_cross_attention=self.only_cross_attention      ,use_memory_efficient_attention=self.use_memory_efficient_attention      ,dtype=self.dtype      ,)
										attentions.append(snake_case__ )

							SCREAMING_SNAKE_CASE_   : str					  =      resnets
							SCREAMING_SNAKE_CASE_   : Any					  =      attentions

							if self.add_upsample:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      FlaxUpsampleaD(self.out_channels      ,dtype=self.dtype )






				def __call__(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__=True ):
							for resnet, attn in zip(self.resnets      ,self.attentions ):
										# pop res hidden states
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      res_hidden_states_tuple[-1]
										SCREAMING_SNAKE_CASE_   : Dict					  =      res_hidden_states_tuple[:-1]
										SCREAMING_SNAKE_CASE_   : Any					  =      jnp.concatenate((hidden_states, res_hidden_states)      ,axis=-1 )

										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      resnet(snake_case__      ,snake_case__      ,deterministic=snake_case__ )
										SCREAMING_SNAKE_CASE_   : Dict					  =      attn(snake_case__      ,snake_case__      ,deterministic=snake_case__ )

							if self.add_upsample:
										SCREAMING_SNAKE_CASE_   : Tuple					  =      self.upsamplers_a(snake_case__ )

							return hidden_states







class 				lowerCAmelCase_       (					nn.Module							):
				__a     :       int
				__a     :       int
				__a     :       int
				__a     :       float						=     0.0
				__a     :       int						=     1
				__a     :       bool						=     True
				__a     :       jnp.dtype						=     jnp.floataa






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      []

							for i in range(self.num_layers ):
										SCREAMING_SNAKE_CASE_   : int					  =      self.in_channels if (i == self.num_layers - 1) else self.out_channels
										SCREAMING_SNAKE_CASE_   : Any					  =      self.prev_output_channel if i == 0 else self.out_channels

										SCREAMING_SNAKE_CASE_   : List[str]					  =      FlaxResnetBlockaD(
										    in_channels=resnet_in_channels + res_skip_channels      ,out_channels=self.out_channels      ,dropout_prob=self.dropout      ,dtype=self.dtype      ,)
										resnets.append(snake_case__ )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      resnets

							if self.add_upsample:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      FlaxUpsampleaD(self.out_channels      ,dtype=self.dtype )






				def __call__(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__=True ):
							for resnet in self.resnets:
										# pop res hidden states
										SCREAMING_SNAKE_CASE_   : List[str]					  =      res_hidden_states_tuple[-1]
										SCREAMING_SNAKE_CASE_   : str					  =      res_hidden_states_tuple[:-1]
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      jnp.concatenate((hidden_states, res_hidden_states)      ,axis=-1 )

										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      resnet(snake_case__      ,snake_case__      ,deterministic=snake_case__ )

							if self.add_upsample:
										SCREAMING_SNAKE_CASE_   : str					  =      self.upsamplers_a(snake_case__ )

							return hidden_states







class 				lowerCAmelCase_       (					nn.Module							):
				__a     :       int
				__a     :       float						=     0.0
				__a     :       int						=     1
				__a     :       int						=     1
				__a     :       bool						=     False
				__a     :       bool						=     False
				__a     :       jnp.dtype						=     jnp.floataa






				def 		snake_case	(			self ):
							# there is always at least one resnet
							SCREAMING_SNAKE_CASE_   : Any					  =      [
							    FlaxResnetBlockaD(
							        in_channels=self.in_channels      ,out_channels=self.in_channels      ,dropout_prob=self.dropout      ,dtype=self.dtype      ,)
							]

							SCREAMING_SNAKE_CASE_   : Tuple					  =      []

							for _ in range(self.num_layers ):
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      FlaxTransformeraDModel(
										    in_channels=self.in_channels      ,n_heads=self.num_attention_heads      ,d_head=self.in_channels // self.num_attention_heads      ,depth=1      ,use_linear_projection=self.use_linear_projection      ,use_memory_efficient_attention=self.use_memory_efficient_attention      ,dtype=self.dtype      ,)
										attentions.append(snake_case__ )

										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      FlaxResnetBlockaD(
										    in_channels=self.in_channels      ,out_channels=self.in_channels      ,dropout_prob=self.dropout      ,dtype=self.dtype      ,)
										resnets.append(snake_case__ )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      resnets
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      attentions






				def __call__(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__=True ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.resnets[0](snake_case__      ,snake_case__ )
							for attn, resnet in zip(self.attentions      ,self.resnets[1:] ):
										SCREAMING_SNAKE_CASE_   : Tuple					  =      attn(snake_case__      ,snake_case__      ,deterministic=snake_case__ )
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      resnet(snake_case__      ,snake_case__      ,deterministic=snake_case__ )

							return hidden_states







class 	__lowercase					:





      def __init__(		self    :       Optional[int]							)     -> int:






         """simple docstring"""







         UpperCAmelCase      					=  {}





      def 							_lowercase					(		self    :       str							)     -> None:






         """simple docstring"""







         print(self.vertex							)
         for i in self.vertex:
            print(__lowerCamelCase       ,							""" -> """       ,							""" -> """.join([str(__lowerCamelCase							) for j in self.vertex[i]]							)							)





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       int							)     -> None:






         """simple docstring"""







         if from_vertex in self.vertex:
            self.vertex[from_vertex].append(__lowerCamelCase							)
         else:
            # else make a new vertex
            UpperCAmelCase      					=  [to_vertex]





      def 							_lowercase					(		self    :       str							)     -> None:






         """simple docstring"""







         UpperCAmelCase      					=  [False] * len(self.vertex							)

         # call the recursive helper function
         for i in range(len(self.vertex							)							):
            if not visited[i]:
               self.dfs_recursive(__lowerCamelCase       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       Optional[int]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       list							)     -> None:






         """simple docstring"""







         UpperCAmelCase      					=  True

         print(__lowerCamelCase       ,							end=""" """							)

         # Recur for all the vertices that are adjacent to this node
         for i in self.vertex:
            if not visited[i]:
               self.dfs_recursive(__lowerCamelCase       ,							__lowerCamelCase							)


if __name__ == "__main__":
 __a	 =      Graph()
 g.add_edge(0, 1)
 g.add_edge(0, 2)
 g.add_edge(1, 2)
 g.add_edge(2, 0)
 g.add_edge(2, 3)
 g.add_edge(3, 3)

 g.print_graph()
 print("""DFS:""")
 g.dfs()

 # OUTPUT:
 # 0  ->  1 -> 2
 # 1  ->  2
 # 2  ->  0 -> 3
 # 3  ->  3
 # DFS:
 #  0 1 2 3



