
'''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			):
 lowerCamelCase				       =42
 lowerCamelCase				       =42
 lowerCamelCase				       =0.0
 lowerCamelCase				       =1
 lowerCamelCase				       =1
 lowerCamelCase				       =True
 lowerCamelCase				       =False
 lowerCamelCase				       =False
 lowerCamelCase				       =False
 lowerCamelCase				       =jnp.floataa
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     Optional[int]												=		[]
   __A		:     Union[str, Any]												=		[]

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

     __A		:     Dict												=		FlaxResnetBlockaD(
         in_channels=lowerCamelCase				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
     resnets.append(lowerCamelCase       )

     __A		:     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(lowerCamelCase       )

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

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






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



   """simple docstring"""





   __A		:     int												=		()

   for resnet, attn in zip(self.resnets				,   self.attentions       ):
     __A		:     int												=		resnet(lowerCamelCase				,   lowerCamelCase				,   deterministic=lowerCamelCase       )
     __A		:     Dict												=		attn(lowerCamelCase				,   lowerCamelCase				,   deterministic=lowerCamelCase       )
     output_states += (hidden_states,)

   if self.add_downsample:
     __A		:     int												=		self.downsamplers_a(lowerCamelCase       )
     output_states += (hidden_states,)

   return hidden_states, output_states







class  __A				( nn.Module			):
 lowerCamelCase				       =42
 lowerCamelCase				       =42
 lowerCamelCase				       =0.0
 lowerCamelCase				       =1
 lowerCamelCase				       =True
 lowerCamelCase				       =jnp.floataa
 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   __A		:     str												=		[]

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

     __A		:     int												=		FlaxResnetBlockaD(
         in_channels=lowerCamelCase				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
     resnets.append(lowerCamelCase       )
   __A		:     Any												=		resnets

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






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



   """simple docstring"""





   __A		:     Union[str, Any]												=		()

   for resnet in self.resnets:
     __A		:     Any												=		resnet(lowerCamelCase				,   lowerCamelCase				,   deterministic=lowerCamelCase       )
     output_states += (hidden_states,)

   if self.add_downsample:
     __A		:     str												=		self.downsamplers_a(lowerCamelCase       )
     output_states += (hidden_states,)

   return hidden_states, output_states







class  __A				( nn.Module			):
 lowerCamelCase				       =42
 lowerCamelCase				       =42
 lowerCamelCase				       =42
 lowerCamelCase				       =0.0
 lowerCamelCase				       =1
 lowerCamelCase				       =1
 lowerCamelCase				       =True
 lowerCamelCase				       =False
 lowerCamelCase				       =False
 lowerCamelCase				       =False
 lowerCamelCase				       =jnp.floataa
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   __A		:     List[str]												=		[]
   __A		:     Union[str, Any]												=		[]

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

     __A		:     List[Any]												=		FlaxResnetBlockaD(
         in_channels=resnet_in_channels + res_skip_channels				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
     resnets.append(lowerCamelCase       )

     __A		:     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(lowerCamelCase       )

   __A		:     Union[str, Any]												=		resnets
   __A		:     Any												=		attentions

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






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



   """simple docstring"""





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

     __A		:     Union[str, Any]												=		resnet(lowerCamelCase				,   lowerCamelCase				,   deterministic=lowerCamelCase       )
     __A		:     Optional[Any]												=		attn(lowerCamelCase				,   lowerCamelCase				,   deterministic=lowerCamelCase       )

   if self.add_upsample:
     __A		:     int												=		self.upsamplers_a(lowerCamelCase       )

   return hidden_states







class  __A				( nn.Module			):
 lowerCamelCase				       =42
 lowerCamelCase				       =42
 lowerCamelCase				       =42
 lowerCamelCase				       =0.0
 lowerCamelCase				       =1
 lowerCamelCase				       =True
 lowerCamelCase				       =jnp.floataa
 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   __A		:     Tuple												=		[]

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

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

   __A		:     Any												=		resnets

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






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



   """simple docstring"""





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

     __A		:     str												=		resnet(lowerCamelCase				,   lowerCamelCase				,   deterministic=lowerCamelCase       )

   if self.add_upsample:
     __A		:     Dict												=		self.upsamplers_a(lowerCamelCase       )

   return hidden_states







class  __A				( nn.Module			):
 lowerCamelCase				       =42
 lowerCamelCase				       =0.0
 lowerCamelCase				       =1
 lowerCamelCase				       =1
 lowerCamelCase				       =False
 lowerCamelCase				       =False
 lowerCamelCase				       =jnp.floataa
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





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

   __A		:     List[Any]												=		[]

   for _ in range(self.num_layers       ):
     __A		:     List[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(lowerCamelCase       )

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

   __A		:     Optional[Any]												=		resnets
   __A		:     Union[str, Any]												=		attentions






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



   """simple docstring"""





   __A		:     Dict												=		self.resnets[0](lowerCamelCase				,   lowerCamelCase       )
   for attn, resnet in zip(self.attentions				,   self.resnets[1:]       ):
     __A		:     List[Any]												=		attn(lowerCamelCase				,   lowerCamelCase				,   deterministic=lowerCamelCase       )
     __A		:     Any												=		resnet(lowerCamelCase				,   lowerCamelCase				,   deterministic=lowerCamelCase       )

   return hidden_states





'''simple docstring'''
A__   :		List[Any] 		=[
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages



