


'''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   UpperCAmelCase_    (nn.Module    ):





    """simple docstring"""






    lowerCamelCase    :   int
    lowerCamelCase    :   int
    lowerCamelCase    :   float					=     0.0
    lowerCamelCase    :   int					=     1
    lowerCamelCase    :   int					=     1
    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   jnp.dtype					=     jnp.floataa







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      Any														=						[]
         __lowerCamelCase  :      List[str]														=						[]

         for i in range(self.num_layers      ):
              __lowerCamelCase  :      Optional[Any]														=						self.in_channels if i == 0 else self.out_channels

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

              __lowerCamelCase  :      Optional[int]														=						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(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Dict														=						resnets
         __lowerCamelCase  :      str														=						attentions

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







    def __call__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=True      )							->	Dict:
         __lowerCamelCase  :      Dict														=						()

         for resnet, attn in zip(self.resnets     ,	self.attentions      ):
              __lowerCamelCase  :      List[Any]														=						resnet(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	deterministic=SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      List[str]														=						attn(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	deterministic=SCREAMING_SNAKE_CASE_      )
              output_states += (hidden_states,)

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

         return hidden_states, output_states







class   UpperCAmelCase_    (nn.Module    ):





    """simple docstring"""






    lowerCamelCase    :   int
    lowerCamelCase    :   int
    lowerCamelCase    :   float					=     0.0
    lowerCamelCase    :   int					=     1
    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   jnp.dtype					=     jnp.floataa







    def  lowercase_							(				self      )							->	Union[str, Any]:
         __lowerCamelCase  :      Tuple														=						[]

         for i in range(self.num_layers      ):
              __lowerCamelCase  :      Optional[int]														=						self.in_channels if i == 0 else self.out_channels

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

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







    def __call__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=True      )							->	Union[str, Any]:
         __lowerCamelCase  :      int														=						()

         for resnet in self.resnets:
              __lowerCamelCase  :      Dict														=						resnet(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	deterministic=SCREAMING_SNAKE_CASE_      )
              output_states += (hidden_states,)

         if self.add_downsample:
              __lowerCamelCase  :      int														=						self.downsamplers_a(SCREAMING_SNAKE_CASE_      )
              output_states += (hidden_states,)

         return hidden_states, output_states







class   UpperCAmelCase_    (nn.Module    ):





    """simple docstring"""






    lowerCamelCase    :   int
    lowerCamelCase    :   int
    lowerCamelCase    :   int
    lowerCamelCase    :   float					=     0.0
    lowerCamelCase    :   int					=     1
    lowerCamelCase    :   int					=     1
    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   jnp.dtype					=     jnp.floataa







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      Optional[Any]														=						[]
         __lowerCamelCase  :      Dict														=						[]

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

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

              __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(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Optional[int]														=						resnets
         __lowerCamelCase  :      Optional[Any]														=						attentions

         if self.add_upsample:
              __lowerCamelCase  :      Union[str, Any]														=						FlaxUpsampleaD(self.out_channels     ,	dtype=self.dtype      )







    def __call__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=True      )							->	Tuple:
         for resnet, attn in zip(self.resnets     ,	self.attentions      ):
              # pop res hidden states
              __lowerCamelCase  :      int														=						res_hidden_states_tuple[-1]
              __lowerCamelCase  :      str														=						res_hidden_states_tuple[:-1]
              __lowerCamelCase  :      Optional[int]														=						jnp.concatenate((hidden_states, res_hidden_states)     ,	axis=-1      )

              __lowerCamelCase  :      List[str]														=						resnet(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	deterministic=SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Dict														=						attn(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	deterministic=SCREAMING_SNAKE_CASE_      )

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

         return hidden_states







class   UpperCAmelCase_    (nn.Module    ):





    """simple docstring"""






    lowerCamelCase    :   int
    lowerCamelCase    :   int
    lowerCamelCase    :   int
    lowerCamelCase    :   float					=     0.0
    lowerCamelCase    :   int					=     1
    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   jnp.dtype					=     jnp.floataa







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      Tuple														=						[]

         for i in range(self.num_layers      ):
              __lowerCamelCase  :      Dict														=						self.in_channels if (i == self.num_layers - 1) else self.out_channels
              __lowerCamelCase  :      str														=						self.prev_output_channel if i == 0 else self.out_channels

              __lowerCamelCase  :      Optional[int]														=						FlaxResnetBlockaD(
                  in_channels=resnet_in_channels + res_skip_channels     ,	out_channels=self.out_channels     ,	dropout_prob=self.dropout     ,	dtype=self.dtype     ,	)
              resnets.append(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      List[str]														=						resnets

         if self.add_upsample:
              __lowerCamelCase  :      Tuple														=						FlaxUpsampleaD(self.out_channels     ,	dtype=self.dtype      )







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

              __lowerCamelCase  :      Optional[int]														=						resnet(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	deterministic=SCREAMING_SNAKE_CASE_      )

         if self.add_upsample:
              __lowerCamelCase  :      int														=						self.upsamplers_a(SCREAMING_SNAKE_CASE_      )

         return hidden_states







class   UpperCAmelCase_    (nn.Module    ):





    """simple docstring"""






    lowerCamelCase    :   int
    lowerCamelCase    :   float					=     0.0
    lowerCamelCase    :   int					=     1
    lowerCamelCase    :   int					=     1
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   jnp.dtype					=     jnp.floataa







    def  lowercase_							(				self      )							->	Optional[Any]:
         # there is always at least one resnet
         __lowerCamelCase  :      Dict														=						[
             FlaxResnetBlockaD(
                 in_channels=self.in_channels     ,	out_channels=self.in_channels     ,	dropout_prob=self.dropout     ,	dtype=self.dtype     ,	)
         ]

         __lowerCamelCase  :      List[str]														=						[]

         for _ in range(self.num_layers      ):
              __lowerCamelCase  :      List[str]														=						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(SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      List[Any]														=						FlaxResnetBlockaD(
                  in_channels=self.in_channels     ,	out_channels=self.in_channels     ,	dropout_prob=self.dropout     ,	dtype=self.dtype     ,	)
              resnets.append(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Tuple														=						resnets
         __lowerCamelCase  :      Optional[int]														=						attentions







    def __call__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=True      )							->	Dict:
         __lowerCamelCase  :      List[str]														=						self.resnets[0](SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         for attn, resnet in zip(self.attentions     ,	self.resnets[1:]      ):
              __lowerCamelCase  :      Optional[int]														=						attn(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	deterministic=SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Tuple														=						resnet(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	deterministic=SCREAMING_SNAKE_CASE_      )

         return hidden_states










'''simple docstring'''



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 1_00				)	->						int:
     __lowerCamelCase  :      Union[str, Any]														=						n * (n + 1) * (2 * n + 1) / 6
     __lowerCamelCase  :      Union[str, Any]														=						(n * (n + 1) / 2) ** 2
     return int(square_of_sum - sum_of_squares				)


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






