





'''simple docstring'''


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							):
     __A      =			42
     __A      =			42
     __A      =			0.0
     __A      =			1
     __A      =			1
     __A      =			True
     __A      =			False
     __A      =			False
     __A      =			False
     __A      =			jnp.floataa





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:int    			= []
        lowercase_			:int    			= []

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

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

           lowercase_			:List[str]    			= 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						)

        lowercase_			:Tuple    			= resnets
        lowercase_			:Optional[Any]    			= attentions

        if self.add_downsample:
           lowercase_			:str    			= FlaxDownsampleaD(self.out_channels	,      dtype=self.dtype						)







     def __call__( self		:					Optional[int]	,      lowercase		:					Optional[Any]	,      lowercase		:					Union[str, Any]	,      lowercase		:					Union[str, Any]	,      lowercase		:					Any=True						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= ()

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

        if self.add_downsample:
           lowercase_			:Dict    			= self.downsamplers_a(lowercase						)
           output_states += (hidden_states,)

        return hidden_states, output_states







class       a_   (				nn.Module							):
     __A      =			42
     __A      =			42
     __A      =			0.0
     __A      =			1
     __A      =			True
     __A      =			jnp.floataa





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:Tuple    			= []

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

           lowercase_			:int    			= FlaxResnetBlockaD(
               in_channels=lowercase	,      out_channels=self.out_channels	,      dropout_prob=self.dropout	,      dtype=self.dtype	,      )
           resnets.append(lowercase						)
        lowercase_			:List[str]    			= resnets

        if self.add_downsample:
           lowercase_			:Optional[int]    			= FlaxDownsampleaD(self.out_channels	,      dtype=self.dtype						)







     def __call__( self		:					int	,      lowercase		:					Any	,      lowercase		:					int	,      lowercase		:					int=True						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= ()

        for resnet in self.resnets:
           lowercase_			:Optional[int]    			= resnet(lowercase	,      lowercase	,      deterministic=lowercase						)
           output_states += (hidden_states,)

        if self.add_downsample:
           lowercase_			:Union[str, Any]    			= self.downsamplers_a(lowercase						)
           output_states += (hidden_states,)

        return hidden_states, output_states







class       a_   (				nn.Module							):
     __A      =			42
     __A      =			42
     __A      =			42
     __A      =			0.0
     __A      =			1
     __A      =			1
     __A      =			True
     __A      =			False
     __A      =			False
     __A      =			False
     __A      =			jnp.floataa





     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        lowercase_			:Any    			= []
        lowercase_			:int    			= []

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

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

           lowercase_			: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(lowercase						)

        lowercase_			:List[str]    			= resnets
        lowercase_			:Optional[Any]    			= attentions

        if self.add_upsample:
           lowercase_			:Dict    			= FlaxUpsampleaD(self.out_channels	,      dtype=self.dtype						)







     def __call__( self		:					int	,      lowercase		:					Optional[Any]	,      lowercase		:					List[str]	,      lowercase		:					List[Any]	,      lowercase		:					Union[str, Any]	,      lowercase		:					Dict=True						):





        """simple docstring"""




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

           lowercase_			:Any    			= resnet(lowercase	,      lowercase	,      deterministic=lowercase						)
           lowercase_			:Tuple    			= attn(lowercase	,      lowercase	,      deterministic=lowercase						)

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

        return hidden_states







class       a_   (				nn.Module							):
     __A      =			42
     __A      =			42
     __A      =			42
     __A      =			0.0
     __A      =			1
     __A      =			True
     __A      =			jnp.floataa





     def 							lowercase__      ( self		:					int						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= []

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

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

        lowercase_			:Optional[int]    			= resnets

        if self.add_upsample:
           lowercase_			:int    			= FlaxUpsampleaD(self.out_channels	,      dtype=self.dtype						)







     def __call__( self		:					Optional[int]	,      lowercase		:					List[str]	,      lowercase		:					str	,      lowercase		:					Union[str, Any]	,      lowercase		:					int=True						):





        """simple docstring"""




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

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

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

        return hidden_states







class       a_   (				nn.Module							):
     __A      =			42
     __A      =			0.0
     __A      =			1
     __A      =			1
     __A      =			False
     __A      =			False
     __A      =			jnp.floataa





     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




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

        lowercase_			:Tuple    			= []

        for _ in range(self.num_layers						):
           lowercase_			:Any    			= 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						)

           lowercase_			:Optional[int]    			= FlaxResnetBlockaD(
               in_channels=self.in_channels	,      out_channels=self.in_channels	,      dropout_prob=self.dropout	,      dtype=self.dtype	,      )
           resnets.append(lowercase						)

        lowercase_			:Optional[int]    			= resnets
        lowercase_			:Union[str, Any]    			= attentions







     def __call__( self		:					Optional[Any]	,      lowercase		:					List[str]	,      lowercase		:					Tuple	,      lowercase		:					Any	,      lowercase		:					List[str]=True						):





        """simple docstring"""




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

        return hidden_states









'''simple docstring'''


def 							UpperCAmelCase_  (						__lowerCamelCase					:					int = 1_00 ):
   lowercase_			:Tuple    			= n * (n + 1) * (2 * n + 1) / 6
   lowercase_			:List[str]    			= (n * (n + 1) / 2) ** 2
   return int(square_of_sum - sum_of_squares )


if __name__ == "__main__":
 print(F'''{solution() = }''')


