"""simple docstring"""




# using dfs for finding eulerian path traversal










def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__=None ):






							"""simple docstring"""





							A__		   =							(path or []) + [u]
							for v in graph[u]:
														if visited_edge[u][v] is False:
																					A__		   =							True, True
																					A__		   =							dfs(SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__ )
							return path






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							0
							A__		   =							-1
							for i in range(SCREAMING_SNAKE_CASE__ ):
														if i not in graph.keys():
																					continue
														if len(graph[i] ) % 2 == 1:
																					odd_degree_nodes += 1
																					A__		   =							i
							if odd_degree_nodes == 0:
														return 1, odd_node
							if odd_degree_nodes == 2:
														return 2, odd_node
							return 3, odd_node






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							[[False for _ in range(max_node + 1 )] for _ in range(max_node + 1 )]
							A__		   =							check_circuit_or_path(SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__ )
							if check == 3:
														print('graph is not Eulerian' )
														print('no path' )
														return
							A__		   =							1
							if check == 2:
														A__		   =							odd_node
														print('graph has a Euler path' )
							if check == 1:
														print('graph has a Euler cycle' )
							A__		   =							dfs(SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__ )
							print(SCREAMING_SNAKE_CASE__ )






def      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
							A__		   =							{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
							A__		   =							{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
							A__		   =							{1: [2, 3], 2: [1, 3], 3: [1, 2]}
							A__		   =							{
							    1: [],
							    2: []
							    # all degree is zero
							}
							A__		   =							10
							check_euler(SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__ )
							check_euler(SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__ )
							check_euler(SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__ )
							check_euler(SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__ )
							check_euler(SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
				main()









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

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




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






      def        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		str		=[]
             _lowerCamelCase       :		Dict		=[]

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

                    _lowerCamelCase       :		Any		=FlaxResnetBlockaD(
                        in_channels=lowercase_			,   out_channels=self.out_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )

                    _lowerCamelCase       :		Dict		=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(lowercase_ )

             _lowerCamelCase       :		Optional[int]		=resnets
             _lowerCamelCase       :		Dict		=attentions

             if self.add_downsample:
                    _lowerCamelCase       :		Union[str, Any]		=FlaxDownsampleaD(self.out_channels			,   dtype=self.dtype )






      def __call__(					self						:     Any			,   lowercase_						:     Optional[Any]			,   lowercase_						:     List[Any]			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     List[str]=True )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=()

             for resnet, attn in zip(self.resnets			,   self.attentions ):
                    _lowerCamelCase       :		Union[str, Any]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    _lowerCamelCase       :		Union[str, Any]		=attn(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    output_states += (hidden_states,)

             if self.add_downsample:
                    _lowerCamelCase       :		Optional[int]		=self.downsamplers_a(lowercase_ )
                    output_states += (hidden_states,)

             return hidden_states, output_states




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






      def        lowerCamelCase       (					self						:     Optional[int] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		str		=[]

             for i in range(self.num_layers ):
                    _lowerCamelCase       :		Tuple		=self.in_channels if i == 0 else self.out_channels

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

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






      def __call__(					self						:     Any			,   lowercase_						:     Dict			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Optional[Any]=True )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=()

             for resnet in self.resnets:
                    _lowerCamelCase       :		Tuple		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    output_states += (hidden_states,)

             if self.add_downsample:
                    _lowerCamelCase       :		Tuple		=self.downsamplers_a(lowercase_ )
                    output_states += (hidden_states,)

             return hidden_states, output_states




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






      def        lowerCamelCase       (					self						:     Dict )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		str		=[]
             _lowerCamelCase       :		List[str]		=[]

             for i in range(self.num_layers ):
                    _lowerCamelCase       :		List[str]		=self.in_channels if (i == self.num_layers - 1) else self.out_channels
                    _lowerCamelCase       :		Tuple		=self.prev_output_channel if i == 0 else self.out_channels

                    _lowerCamelCase       :		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(lowercase_ )

                    _lowerCamelCase       :		Tuple		=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(lowercase_ )

             _lowerCamelCase       :		int		=resnets
             _lowerCamelCase       :		Dict		=attentions

             if self.add_upsample:
                    _lowerCamelCase       :		str		=FlaxUpsampleaD(self.out_channels			,   dtype=self.dtype )






      def __call__(					self						:     Optional[Any]			,   lowercase_						:     Any			,   lowercase_						:     Any			,   lowercase_						:     List[Any]			,   lowercase_						:     Dict			,   lowercase_						:     Union[str, Any]=True )					->						Optional[int]:
             """simple docstring"""




             for resnet, attn in zip(self.resnets			,   self.attentions ):
                    # pop res hidden states
                    _lowerCamelCase       :		Optional[int]		=res_hidden_states_tuple[-1]
                    _lowerCamelCase       :		Union[str, Any]		=res_hidden_states_tuple[:-1]
                    _lowerCamelCase       :		Any		=jnp.concatenate((hidden_states, res_hidden_states)			,   axis=-1 )

                    _lowerCamelCase       :		Optional[Any]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    _lowerCamelCase       :		List[Any]		=attn(lowercase_			,   lowercase_			,   deterministic=lowercase_ )

             if self.add_upsample:
                    _lowerCamelCase       :		Optional[Any]		=self.upsamplers_a(lowercase_ )

             return hidden_states




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






      def        lowerCamelCase       (					self						:     List[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=[]

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

                    _lowerCamelCase       :		str		=FlaxResnetBlockaD(
                        in_channels=resnet_in_channels + res_skip_channels			,   out_channels=self.out_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )

             _lowerCamelCase       :		str		=resnets

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






      def __call__(					self						:     List[str]			,   lowercase_						:     str			,   lowercase_						:     Dict			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Any=True )					->						int:
             """simple docstring"""




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

                    _lowerCamelCase       :		Optional[Any]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )

             if self.add_upsample:
                    _lowerCamelCase       :		Union[str, Any]		=self.upsamplers_a(lowercase_ )

             return hidden_states




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






      def        lowerCamelCase       (					self						:     int )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=[
                 FlaxResnetBlockaD(
                     in_channels=self.in_channels			,   out_channels=self.in_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
             ]

             _lowerCamelCase       :		Any		=[]

             for _ in range(self.num_layers ):
                    _lowerCamelCase       :		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(lowercase_ )

                    _lowerCamelCase       :		Tuple		=FlaxResnetBlockaD(
                        in_channels=self.in_channels			,   out_channels=self.in_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )

             _lowerCamelCase       :		List[Any]		=resnets
             _lowerCamelCase       :		List[str]		=attentions






      def __call__(					self						:     Optional[int]			,   lowercase_						:     List[Any]			,   lowercase_						:     Dict			,   lowercase_						:     Tuple			,   lowercase_						:     List[str]=True )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=self.resnets[0](lowercase_			,   lowercase_ )
             for attn, resnet in zip(self.attentions			,   self.resnets[1:] ):
                    _lowerCamelCase       :		Tuple		=attn(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    _lowerCamelCase       :		List[str]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )

             return hidden_states


