


"""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    lowerCamelCase__						(		nn.Module    ):
      a :     int
      a :     int
      a :     float											=		0.0
      a :     int											=		1
      a :     int											=		1
      a :     bool											=		True
      a :     bool											=		False
      a :     bool											=		False
      a :     bool											=		False
      a :     jnp.dtype											=		jnp.floataa
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase									=	[]
       __lowercase									=	[]

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

        __lowercase									=	FlaxResnetBlockaD(
            in_channels=A_      ,       out_channels=self.out_channels      ,       dropout_prob=self.dropout      ,       dtype=self.dtype      ,       )
        resnets.append(A_							)

        __lowercase									=	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_							)

       __lowercase									=	resnets
       __lowercase									=	attentions

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






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






       '''simple docstring'''


       __lowercase									=	()

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

       if self.add_downsample:
        __lowercase									=	self.downsamplers_a(A_							)
        output_states += (hidden_states,)

       return hidden_states, output_states






class    lowerCamelCase__						(		nn.Module    ):
      a :     int
      a :     int
      a :     float											=		0.0
      a :     int											=		1
      a :     bool											=		True
      a :     jnp.dtype											=		jnp.floataa
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	[]

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

        __lowercase									=	FlaxResnetBlockaD(
            in_channels=A_      ,       out_channels=self.out_channels      ,       dropout_prob=self.dropout      ,       dtype=self.dtype      ,       )
        resnets.append(A_							)
       __lowercase									=	resnets

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






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






       '''simple docstring'''


       __lowercase									=	()

       for resnet in self.resnets:
        __lowercase									=	resnet(A_      ,       A_      ,       deterministic=A_							)
        output_states += (hidden_states,)

       if self.add_downsample:
        __lowercase									=	self.downsamplers_a(A_							)
        output_states += (hidden_states,)

       return hidden_states, output_states






class    lowerCamelCase__						(		nn.Module    ):
      a :     int
      a :     int
      a :     int
      a :     float											=		0.0
      a :     int											=		1
      a :     int											=		1
      a :     bool											=		True
      a :     bool											=		False
      a :     bool											=		False
      a :     bool											=		False
      a :     jnp.dtype											=		jnp.floataa
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase									=	[]
       __lowercase									=	[]

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

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

        __lowercase									=	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_							)

       __lowercase									=	resnets
       __lowercase									=	attentions

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






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






       '''simple docstring'''


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

        __lowercase									=	resnet(A_      ,       A_      ,       deterministic=A_							)
        __lowercase									=	attn(A_      ,       A_      ,       deterministic=A_							)

       if self.add_upsample:
        __lowercase									=	self.upsamplers_a(A_							)

       return hidden_states






class    lowerCamelCase__						(		nn.Module    ):
      a :     int
      a :     int
      a :     int
      a :     float											=		0.0
      a :     int											=		1
      a :     bool											=		True
      a :     jnp.dtype											=		jnp.floataa
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	[]

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

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

       __lowercase									=	resnets

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






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






       '''simple docstring'''


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

        __lowercase									=	resnet(A_      ,       A_      ,       deterministic=A_							)

       if self.add_upsample:
        __lowercase									=	self.upsamplers_a(A_							)

       return hidden_states






class    lowerCamelCase__						(		nn.Module    ):
      a :     int
      a :     float											=		0.0
      a :     int											=		1
      a :     int											=		1
      a :     bool											=		False
      a :     bool											=		False
      a :     jnp.dtype											=		jnp.floataa
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	[
           FlaxResnetBlockaD(
               in_channels=self.in_channels      ,       out_channels=self.in_channels      ,       dropout_prob=self.dropout      ,       dtype=self.dtype      ,       )
       ]

       __lowercase									=	[]

       for _ in range(self.num_layers							):
        __lowercase									=	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_							)

        __lowercase									=	FlaxResnetBlockaD(
            in_channels=self.in_channels      ,       out_channels=self.in_channels      ,       dropout_prob=self.dropout      ,       dtype=self.dtype      ,       )
        resnets.append(A_							)

       __lowercase									=	resnets
       __lowercase									=	attentions






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






       '''simple docstring'''


       __lowercase									=	self.resnets[0](A_      ,       A_							)
       for attn, resnet in zip(self.attentions      ,       self.resnets[1:]							):
        __lowercase									=	attn(A_      ,       A_      ,       deterministic=A_							)
        __lowercase									=	resnet(A_      ,       A_      ,       deterministic=A_							)

       return hidden_states




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )
 if n == 0:
  return 0
 __lowercase									=	float("""-inf""" )
 for i in range(1				,   n + 1 ):
  __lowercase									=	max(
      UpperCamelCase__				,   prices[i - 1] + naive_cut_rod_recursive(n - i				,   UpperCamelCase__ ) )

 return max_revue


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	[float("""-inf""" ) for _ in range(n + 1 )]
 return _top_down_cut_rod_recursive(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list				,   UpperCamelCase__	: list ):






 """simple docstring"""







 if max_rev[n] >= 0:
  return max_rev[n]
 elif n == 0:
  return 0
 else:
  __lowercase									=	float("""-inf""" )
  for i in range(1				,   n + 1 ):
   __lowercase									=	max(
       UpperCamelCase__				,   prices[i - 1] + _top_down_cut_rod_recursive(n - i				,   UpperCamelCase__				,   UpperCamelCase__ )				,   )

  __lowercase									=	max_revenue

 return max_rev[n]


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )

 # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
 # length 0.
 __lowercase									=	[float("""-inf""" ) for _ in range(n + 1 )]
 __lowercase									=	0

 for i in range(1				,   n + 1 ):
  __lowercase									=	max_rev[i]
  for j in range(1				,   i + 1 ):
   __lowercase									=	max(UpperCamelCase__				,   prices[j - 1] + max_rev[i - j] )

  __lowercase									=	max_revenue_i

 return max_rev[n]


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 if n < 0:
  __lowercase									=	f'''n must be greater than or equal to 0. Got n = {n}'''
  raise ValueError(UpperCamelCase__ )

 if n > len(UpperCamelCase__ ):
  __lowercase									=	(
      """Each integral piece of rod must have a corresponding price. """
      f'''Got n = {n} but length of prices = {len(UpperCamelCase__ )}'''
  )
  raise ValueError(UpperCamelCase__ )


def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	[6, 10, 12, 15, 20, 23]
 __lowercase									=	len(UpperCamelCase__ )

 # the best revenue comes from cutting the rod into 6 pieces, each
 # of length 1 resulting in a revenue of 6 * 6 = 36.
 __lowercase									=	36

 __lowercase									=	top_down_cut_rod(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	bottom_up_cut_rod(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	naive_cut_rod_recursive(UpperCamelCase__				,   UpperCamelCase__ )

 assert expected_max_revenue == max_rev_top_down
 assert max_rev_top_down == max_rev_bottom_up
 assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
       main()
