



"""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"""




      _SCREAMING_SNAKE_CASE  				=     42
      _SCREAMING_SNAKE_CASE  				=     42
      _SCREAMING_SNAKE_CASE  				=     0.0
      _SCREAMING_SNAKE_CASE  				=     1
      _SCREAMING_SNAKE_CASE  				=     1
      _SCREAMING_SNAKE_CASE  				=     True
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     jnp.floataa



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				Union[str, Any]								= []
        lowerCAmelCase_   :				List[Any]								= []

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

          lowerCAmelCase_   :				List[str]								= 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[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(SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				List[Any]								= resnets
        lowerCAmelCase_   :				Optional[Any]								= attentions

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





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

        for resnet, attn in zip(self.resnets    ,			self.attentions       ):
          lowerCAmelCase_   :				Tuple								= resnet(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			deterministic=SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Tuple								= attn(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			deterministic=SCREAMING_SNAKE_CASE_       )
          output_states += (hidden_states,)

        if self.add_downsample:
          lowerCAmelCase_   :				Any								= self.downsamplers_a(SCREAMING_SNAKE_CASE_       )
          output_states += (hidden_states,)

        return hidden_states, output_states







class       UpperCamelCase__   (	nn.Module  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     42
      _SCREAMING_SNAKE_CASE  				=     42
      _SCREAMING_SNAKE_CASE  				=     0.0
      _SCREAMING_SNAKE_CASE  				=     1
      _SCREAMING_SNAKE_CASE  				=     True
      _SCREAMING_SNAKE_CASE  				=     jnp.floataa



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        lowerCAmelCase_   :				Any								= []

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

          lowerCAmelCase_   :				List[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_   :				str								= resnets

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





      def __call__(      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Tuple=True       ):
        lowerCAmelCase_   :				Tuple								= ()

        for resnet in self.resnets:
          lowerCAmelCase_   :				Union[str, Any]								= resnet(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"""




      _SCREAMING_SNAKE_CASE  				=     42
      _SCREAMING_SNAKE_CASE  				=     42
      _SCREAMING_SNAKE_CASE  				=     42
      _SCREAMING_SNAKE_CASE  				=     0.0
      _SCREAMING_SNAKE_CASE  				=     1
      _SCREAMING_SNAKE_CASE  				=     1
      _SCREAMING_SNAKE_CASE  				=     True
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     jnp.floataa



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        lowerCAmelCase_   :				Optional[int]								= []
        lowerCAmelCase_   :				Dict								= []

        for i in range(self.num_layers       ):
          lowerCAmelCase_   :				Dict								= self.in_channels if (i == self.num_layers - 1) else self.out_channels
          lowerCAmelCase_   :				Dict								= 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_   :				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_   :				Optional[int]								= resnets
        lowerCAmelCase_   :				Optional[Any]								= attentions

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





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

          lowerCAmelCase_   :				Dict								= resnet(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			deterministic=SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Any								= attn(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			deterministic=SCREAMING_SNAKE_CASE_       )

        if self.add_upsample:
          lowerCAmelCase_   :				Optional[int]								= self.upsamplers_a(SCREAMING_SNAKE_CASE_       )

        return hidden_states







class       UpperCamelCase__   (	nn.Module  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     42
      _SCREAMING_SNAKE_CASE  				=     42
      _SCREAMING_SNAKE_CASE  				=     42
      _SCREAMING_SNAKE_CASE  				=     0.0
      _SCREAMING_SNAKE_CASE  				=     1
      _SCREAMING_SNAKE_CASE  				=     True
      _SCREAMING_SNAKE_CASE  				=     jnp.floataa



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        lowerCAmelCase_   :				List[Any]								= []

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

        lowerCAmelCase_   :				List[str]								= resnets

        if self.add_upsample:
          lowerCAmelCase_   :				Optional[int]								= FlaxUpsampleaD(self.out_channels    ,			dtype=self.dtype       )





      def __call__(      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=True       ):
        for resnet in self.resnets:
          # pop res hidden states
          lowerCAmelCase_   :				int								= res_hidden_states_tuple[-1]
          lowerCAmelCase_   :				List[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_   :				Tuple								= self.upsamplers_a(SCREAMING_SNAKE_CASE_       )

        return hidden_states







class       UpperCamelCase__   (	nn.Module  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     42
      _SCREAMING_SNAKE_CASE  				=     0.0
      _SCREAMING_SNAKE_CASE  				=     1
      _SCREAMING_SNAKE_CASE  				=     1
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     jnp.floataa



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        # there is always at least one resnet
        lowerCAmelCase_   :				int								= [
            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_   :				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(SCREAMING_SNAKE_CASE_       )

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

        lowerCAmelCase_   :				Any								= resnets
        lowerCAmelCase_   :				int								= attentions





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

        return hidden_states










"""simple docstring"""







import re

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str						)						->   list:

  """simple docstring"""




  return [char.split() for char in re.split(R'[^ a-z A-Z 0-9 \s]'    ,   str_						)]

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str						)						->   str:

  """simple docstring"""




  lowerCAmelCase_   :				str								= split_input(str_						)
  return "".join(
      [''.join([char.capitalize() for char in sub_str]						) for sub_str in string_split]						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				bool    ,   lowerCAmelCase__      :				str						)						->   str:

  """simple docstring"""




  try:
    lowerCAmelCase_   :				int								= split_input(lowerCAmelCase__						)
    if upper:
      lowerCAmelCase_   :				Optional[int]								= ''.join(
          [
              separator.join([char.upper() for char in sub_str]						)
              for sub_str in string_split
          ]						)
    else:
      lowerCAmelCase_   :				Any								= ''.join(
          [
              separator.join([char.lower() for char in sub_str]						)
              for sub_str in string_split
          ]						)
    return res_str
  except IndexError:
    return "not valid string"

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str						)						->   str:

  """simple docstring"""




  return to_simple_case(lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str						)						->   str:

  """simple docstring"""




  try:
    lowerCAmelCase_   :				Dict								= to_simple_case(lowerCAmelCase__						)
    return res_str[0].lower() + res_str[1:]
  except IndexError:
    return "not valid string"

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				bool						)						->   str:

  """simple docstring"""




  return to_complex_case(lowerCAmelCase__    ,   lowerCAmelCase__    ,   '_'						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				bool						)						->   str:

  """simple docstring"""




  return to_complex_case(lowerCAmelCase__    ,   lowerCAmelCase__    ,   '-'						)


if __name__ == "__main__":
     __import__("""doctest""").testmod()





