

'''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 ):
       lowerCAmelCase_												=			42
       lowerCAmelCase_												=			42
       lowerCAmelCase_												=			0.0
       lowerCAmelCase_												=			1
       lowerCAmelCase_												=			1
       lowerCAmelCase_												=			True
       lowerCAmelCase_												=			False
       lowerCAmelCase_												=			False
       lowerCAmelCase_												=			False
       lowerCAmelCase_												=			jnp.floataa







       def       snake_case (				self      :			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=__lowercase    ,   out_channels=self.out_channels    ,   dropout_prob=self.dropout    ,   dtype=self.dtype    ,   )
                 resnets.append(__lowercase   )

                 __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(__lowercase   )

            __lowercase      		=resnets
            __lowercase      		=attentions

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







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







            """simple docstring"""







            __lowercase      		=()

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

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

            return hidden_states, output_states






class 	lowerCAmelCase  (   nn.Module ):
       lowerCAmelCase_												=			42
       lowerCAmelCase_												=			42
       lowerCAmelCase_												=			0.0
       lowerCAmelCase_												=			1
       lowerCAmelCase_												=			True
       lowerCAmelCase_												=			jnp.floataa







       def       snake_case (				self      :			int   ):







            """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=__lowercase    ,   out_channels=self.out_channels    ,   dropout_prob=self.dropout    ,   dtype=self.dtype    ,   )
                 resnets.append(__lowercase   )
            __lowercase      		=resnets

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







       def __call__(				self      :			List[str]    ,   __lowercase      :			Any    ,   __lowercase      :			List[str]    ,   __lowercase      :			List[str]=True   ):







            """simple docstring"""







            __lowercase      		=()

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

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

            return hidden_states, output_states






class 	lowerCAmelCase  (   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       snake_case (				self      :			List[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(__lowercase   )

                 __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(__lowercase   )

            __lowercase      		=resnets
            __lowercase      		=attentions

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







       def __call__(				self      :			Optional[Any]    ,   __lowercase      :			List[Any]    ,   __lowercase      :			Dict    ,   __lowercase      :			Any    ,   __lowercase      :			Tuple    ,   __lowercase      :			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(__lowercase    ,   __lowercase    ,   deterministic=__lowercase   )
                 __lowercase      		=attn(__lowercase    ,   __lowercase    ,   deterministic=__lowercase   )

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

            return hidden_states






class 	lowerCAmelCase  (   nn.Module ):
       lowerCAmelCase_												=			42
       lowerCAmelCase_												=			42
       lowerCAmelCase_												=			42
       lowerCAmelCase_												=			0.0
       lowerCAmelCase_												=			1
       lowerCAmelCase_												=			True
       lowerCAmelCase_												=			jnp.floataa







       def       snake_case (				self      :			Optional[Any]   ):







            """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(__lowercase   )

            __lowercase      		=resnets

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







       def __call__(				self      :			List[Any]    ,   __lowercase      :			List[str]    ,   __lowercase      :			Union[str, Any]    ,   __lowercase      :			Optional[Any]    ,   __lowercase      :			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(__lowercase    ,   __lowercase    ,   deterministic=__lowercase   )

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

            return hidden_states






class 	lowerCAmelCase  (   nn.Module ):
       lowerCAmelCase_												=			42
       lowerCAmelCase_												=			0.0
       lowerCAmelCase_												=			1
       lowerCAmelCase_												=			1
       lowerCAmelCase_												=			False
       lowerCAmelCase_												=			False
       lowerCAmelCase_												=			jnp.floataa







       def       snake_case (				self      :			Any   ):







            """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(__lowercase   )

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

            __lowercase      		=resnets
            __lowercase      		=attentions







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







            """simple docstring"""







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

            return hidden_states





'''simple docstring'''

class 	lowerCAmelCase  :







       def __init__(				self      :			List[Any]    ,   __lowercase      :			str    ,   __lowercase      :			Any    ,   __lowercase      :			str   ):







            """simple docstring"""







            __lowercase      		=name
            __lowercase      		=value
            __lowercase      		=weight







       def __repr__(				self      :			List[str]   ):







            """simple docstring"""







            return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            return self.value







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            return self.name







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            return self.weight







       def       snake_case (				self      :			Tuple   ):







            """simple docstring"""







            return self.value / self.weight






def 					__UpperCamelCase				(						lowercase__    :	Dict,       lowercase__    :	List[str],       lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=[]
     for i in range(len(lowercase__			)			):
          menu.append(Things(name[i],       value[i],       weight[i]			)			)
     return menu

def 					__UpperCamelCase				(						lowercase__    :	List[Any],       lowercase__    :	List[Any],       lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=sorted(lowercase__,       key=lowercase__,       reverse=lowercase__			)
     __lowercase      		=[]
     __lowercase		,  __lowercase      		=0.0, 0.0
     for i in range(len(lowercase__			)			):
          if (total_cost + items_copy[i].get_weight()) <= max_cost:
               result.append(items_copy[i]			)
               total_cost += items_copy[i].get_weight()
               total_value += items_copy[i].get_value()
     return (result, total_value)

def 					__UpperCamelCase				(						):






     '''simple docstring'''







if __name__ == "__main__":
       import doctest

       doctest.testmod()


