




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							):




      """simple docstring"""





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

      def      UpperCAmelCase				(				self     )		->    Any:
         snake_case				:    Union[str, Any]           =	[]
         snake_case				:    List[Any]           =	[]

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

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

            snake_case				:    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(A     )

         snake_case				:    List[str]           =	resnets
         snake_case				:    List[str]           =	attentions

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

      def __call__(				self    ,				A    ,				A    ,				A    ,				A=True     )		->    Optional[Any]:
         snake_case				:    Any           =	()

         for resnet, attn in zip(self.resnets    ,				self.attentions     ):
            snake_case				:    List[Any]           =	resnet(A    ,				A    ,				deterministic=A     )
            snake_case				:    Optional[Any]           =	attn(A    ,				A    ,				deterministic=A     )
            output_states += (hidden_states,)

         if self.add_downsample:
            snake_case				:    List[Any]           =	self.downsamplers_a(A     )
            output_states += (hidden_states,)

         return hidden_states, output_states




class 						__lowercase		(nn.Module							):




      """simple docstring"""





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

      def      UpperCAmelCase				(				self     )		->    List[Any]:
         snake_case				:    List[str]           =	[]

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

            snake_case				:    Tuple           =	FlaxResnetBlockaD(
                in_channels=A    ,				out_channels=self.out_channels    ,				dropout_prob=self.dropout    ,				dtype=self.dtype    ,				)
            resnets.append(A     )
         snake_case				:    Optional[Any]           =	resnets

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

      def __call__(				self    ,				A    ,				A    ,				A=True     )		->    Optional[int]:
         snake_case				:    Dict           =	()

         for resnet in self.resnets:
            snake_case				:    int           =	resnet(A    ,				A    ,				deterministic=A     )
            output_states += (hidden_states,)

         if self.add_downsample:
            snake_case				:    List[str]           =	self.downsamplers_a(A     )
            output_states += (hidden_states,)

         return hidden_states, output_states




class 						__lowercase		(nn.Module							):




      """simple docstring"""





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

      def      UpperCAmelCase				(				self     )		->    Any:
         snake_case				:    Optional[int]           =	[]
         snake_case				:    Optional[Any]           =	[]

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

            snake_case				:    Any           =	FlaxResnetBlockaD(
                in_channels=resnet_in_channels + res_skip_channels    ,				out_channels=self.out_channels    ,				dropout_prob=self.dropout    ,				dtype=self.dtype    ,				)
            resnets.append(A     )

            snake_case				:    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(A     )

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

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

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

            snake_case				:    List[str]           =	resnet(A    ,				A    ,				deterministic=A     )
            snake_case				:    Any           =	attn(A    ,				A    ,				deterministic=A     )

         if self.add_upsample:
            snake_case				:    Any           =	self.upsamplers_a(A     )

         return hidden_states




class 						__lowercase		(nn.Module							):




      """simple docstring"""





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

      def      UpperCAmelCase				(				self     )		->    str:
         snake_case				:    int           =	[]

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

            snake_case				:    Tuple           =	FlaxResnetBlockaD(
                in_channels=resnet_in_channels + res_skip_channels    ,				out_channels=self.out_channels    ,				dropout_prob=self.dropout    ,				dtype=self.dtype    ,				)
            resnets.append(A     )

         snake_case				:    int           =	resnets

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

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

            snake_case				:    str           =	resnet(A    ,				A    ,				deterministic=A     )

         if self.add_upsample:
            snake_case				:    Dict           =	self.upsamplers_a(A     )

         return hidden_states




class 						__lowercase		(nn.Module							):




      """simple docstring"""





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

      def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
         # there is always at least one resnet
         snake_case				:    int           =	[
             FlaxResnetBlockaD(
                 in_channels=self.in_channels    ,				out_channels=self.in_channels    ,				dropout_prob=self.dropout    ,				dtype=self.dtype    ,				)
         ]

         snake_case				:    Dict           =	[]

         for _ in range(self.num_layers     ):
            snake_case				:    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(A     )

            snake_case				:    Optional[Any]           =	FlaxResnetBlockaD(
                in_channels=self.in_channels    ,				out_channels=self.in_channels    ,				dropout_prob=self.dropout    ,				dtype=self.dtype    ,				)
            resnets.append(A     )

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

      def __call__(				self    ,				A    ,				A    ,				A    ,				A=True     )		->    str:
         snake_case				:    Any           =	self.resnets[0](A    ,				A     )
         for attn, resnet in zip(self.attentions    ,				self.resnets[1:]     ):
            snake_case				:    str           =	attn(A    ,				A    ,				deterministic=A     )
            snake_case				:    int           =	resnet(A    ,				A    ,				deterministic=A     )

         return hidden_states











import functools




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       int:

   # Validation
   if not isinstance(lowercase						,lowercase					) or not all(isinstance(lowercase						,lowercase					) for day in days					):
      raise ValueError("""The parameter days should be a list of integers"""					)

   if len(lowercase					) != 3 or not all(isinstance(lowercase						,lowercase					) for cost in costs					):
      raise ValueError("""The parameter costs should be a list of three integers"""					)

   if len(lowercase					) == 0:
      return 0

   if min(lowercase					) <= 0:
      raise ValueError("""All days elements should be greater than 0"""					)

   if max(lowercase					) >= 366:
      raise ValueError("""All days elements should be less than 366"""					)

   snake_case				:    List[str]           =	set(lowercase					)

   @functools.cache
   def dynamic_programming(lowercase					) -> int:
      if index > 365:
         return 0

      if index not in days_set:
         return dynamic_programming(index + 1					)

      return min(
          costs[0] + dynamic_programming(index + 1					)						,costs[1] + dynamic_programming(index + 7					)						,costs[2] + dynamic_programming(index + 30					)						,)

   return dynamic_programming(1					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





