


'''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 					lowercase_     (    nn.Module   ):
    __UpperCAmelCase   			=	42
    __UpperCAmelCase   			=	42
    __UpperCAmelCase   			=	0.0
    __UpperCAmelCase   			=	1
    __UpperCAmelCase   			=	1
    __UpperCAmelCase   			=	True
    __UpperCAmelCase   			=	False
    __UpperCAmelCase   			=	False
    __UpperCAmelCase   			=	False
    __UpperCAmelCase   			=	jnp.floataa


    def    __a							(     self    ):
          UpperCamelCase__										=				[]
          UpperCamelCase__										=				[]

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

                UpperCamelCase__										=				FlaxResnetBlockaD(
                    in_channels=a  ,					out_channels=self.out_channels  ,					dropout_prob=self.dropout  ,					dtype=self.dtype  ,					)
                resnets.append(a    )

                UpperCamelCase__										=				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(a    )

          UpperCamelCase__										=				resnets
          UpperCamelCase__										=				attentions

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


    def __call__(     self  ,					a  ,					a  ,					a  ,					a=True    ):
          UpperCamelCase__										=				()

          for resnet, attn in zip(self.resnets  ,					self.attentions    ):
                UpperCamelCase__										=				resnet(a  ,					a  ,					deterministic=a    )
                UpperCamelCase__										=				attn(a  ,					a  ,					deterministic=a    )
                output_states += (hidden_states,)

          if self.add_downsample:
                UpperCamelCase__										=				self.downsamplers_a(a    )
                output_states += (hidden_states,)

          return hidden_states, output_states

class 					lowercase_     (    nn.Module   ):
    __UpperCAmelCase   			=	42
    __UpperCAmelCase   			=	42
    __UpperCAmelCase   			=	0.0
    __UpperCAmelCase   			=	1
    __UpperCAmelCase   			=	True
    __UpperCAmelCase   			=	jnp.floataa


    def    __a							(     self    ):
          UpperCamelCase__										=				[]

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

                UpperCamelCase__										=				FlaxResnetBlockaD(
                    in_channels=a  ,					out_channels=self.out_channels  ,					dropout_prob=self.dropout  ,					dtype=self.dtype  ,					)
                resnets.append(a    )
          UpperCamelCase__										=				resnets

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


    def __call__(     self  ,					a  ,					a  ,					a=True    ):
          UpperCamelCase__										=				()

          for resnet in self.resnets:
                UpperCamelCase__										=				resnet(a  ,					a  ,					deterministic=a    )
                output_states += (hidden_states,)

          if self.add_downsample:
                UpperCamelCase__										=				self.downsamplers_a(a    )
                output_states += (hidden_states,)

          return hidden_states, output_states

class 					lowercase_     (    nn.Module   ):
    __UpperCAmelCase   			=	42
    __UpperCAmelCase   			=	42
    __UpperCAmelCase   			=	42
    __UpperCAmelCase   			=	0.0
    __UpperCAmelCase   			=	1
    __UpperCAmelCase   			=	1
    __UpperCAmelCase   			=	True
    __UpperCAmelCase   			=	False
    __UpperCAmelCase   			=	False
    __UpperCAmelCase   			=	False
    __UpperCAmelCase   			=	jnp.floataa


    def    __a							(     self    ):
          UpperCamelCase__										=				[]
          UpperCamelCase__										=				[]

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

                UpperCamelCase__										=				FlaxResnetBlockaD(
                    in_channels=resnet_in_channels + res_skip_channels  ,					out_channels=self.out_channels  ,					dropout_prob=self.dropout  ,					dtype=self.dtype  ,					)
                resnets.append(a    )

                UpperCamelCase__										=				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(a    )

          UpperCamelCase__										=				resnets
          UpperCamelCase__										=				attentions

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


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

                UpperCamelCase__										=				resnet(a  ,					a  ,					deterministic=a    )
                UpperCamelCase__										=				attn(a  ,					a  ,					deterministic=a    )

          if self.add_upsample:
                UpperCamelCase__										=				self.upsamplers_a(a    )

          return hidden_states

class 					lowercase_     (    nn.Module   ):
    __UpperCAmelCase   			=	42
    __UpperCAmelCase   			=	42
    __UpperCAmelCase   			=	42
    __UpperCAmelCase   			=	0.0
    __UpperCAmelCase   			=	1
    __UpperCAmelCase   			=	True
    __UpperCAmelCase   			=	jnp.floataa


    def    __a							(     self    ):
          UpperCamelCase__										=				[]

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

                UpperCamelCase__										=				FlaxResnetBlockaD(
                    in_channels=resnet_in_channels + res_skip_channels  ,					out_channels=self.out_channels  ,					dropout_prob=self.dropout  ,					dtype=self.dtype  ,					)
                resnets.append(a    )

          UpperCamelCase__										=				resnets

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


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

                UpperCamelCase__										=				resnet(a  ,					a  ,					deterministic=a    )

          if self.add_upsample:
                UpperCamelCase__										=				self.upsamplers_a(a    )

          return hidden_states

class 					lowercase_     (    nn.Module   ):
    __UpperCAmelCase   			=	42
    __UpperCAmelCase   			=	0.0
    __UpperCAmelCase   			=	1
    __UpperCAmelCase   			=	1
    __UpperCAmelCase   			=	False
    __UpperCAmelCase   			=	False
    __UpperCAmelCase   			=	jnp.floataa


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

          UpperCamelCase__										=				[]

          for _ in range(self.num_layers    ):
                UpperCamelCase__										=				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(a    )

                UpperCamelCase__										=				FlaxResnetBlockaD(
                    in_channels=self.in_channels  ,					out_channels=self.in_channels  ,					dropout_prob=self.dropout  ,					dtype=self.dtype  ,					)
                resnets.append(a    )

          UpperCamelCase__										=				resnets
          UpperCamelCase__										=				attentions


    def __call__(     self  ,					a  ,					a  ,					a  ,					a=True    ):
          UpperCamelCase__										=				self.resnets[0](a  ,					a    )
          for attn, resnet in zip(self.attentions  ,					self.resnets[1:]    ):
                UpperCamelCase__										=				attn(a  ,					a  ,					deterministic=a    )
                UpperCamelCase__										=				resnet(a  ,					a  ,					deterministic=a    )

          return hidden_states









"""simple docstring"""


def _lowerCamelCase	(		_UpperCamelCase = 6008_5147_5143      ):

			'''simple docstring'''




			try:
						__lowerCAmelCase				    =			int(_UpperCamelCase      )
			except (TypeError, ValueError):
						raise TypeError("Parameter n must be int or castable to int."      )
			if n <= 0:
						raise ValueError("Parameter n must be greater than or equal to one."      )
			__lowerCAmelCase				    =			2
			__lowerCAmelCase				    =			0
			if n == 2:
						return 2
			while n > 2:
						while n % i != 0:
									i += 1
						__lowerCAmelCase				    =			i
						while n % i == 0:
									__lowerCAmelCase				    =			n // i
						i += 1
			return int(_UpperCamelCase      )


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




