

from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)


@flax.struct.dataclass
class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							42




@flax_register_to_config
class  A						(							nn.Module    ,							_UpperCAmelCase    ,							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							32
    lowerCamelCase			=							4
    lowerCamelCase			=							4
    lowerCamelCase			=							(
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "DownBlock2D",
    )
    lowerCamelCase			=							("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
    lowerCamelCase			=							False
    lowerCamelCase			=							(3_20, 6_40, 12_80, 12_80)
    lowerCamelCase			=							2
    lowerCamelCase			=							8
    lowerCamelCase			=							None
    lowerCamelCase			=							12_80
    lowerCamelCase			=							0.0
    lowerCamelCase			=							False
    lowerCamelCase			=							jnp.floataa
    lowerCamelCase			=							True
    lowerCamelCase			=							0
    lowerCamelCase			=							False


    def 						snake_case__     (      self :       str,lowercase_ :       jax.random.KeyArray		)->    FrozenDict:





        '''simple docstring'''







        A__          =   (1, self.in_channels, self.sample_size, self.sample_size)
        A__          =   jnp.zeros(lowercase_,dtype=jnp.floataa		)
        A__          =   jnp.ones((1,),dtype=jnp.intaa		)
        A__          =   jnp.zeros((1, 1, self.cross_attention_dim),dtype=jnp.floataa		)

        A__      ,						A__          =   jax.random.split(lowercase_		)
        A__          =   {'params': params_rng, 'dropout': dropout_rng}

        return self.init(lowercase_,lowercase_,lowercase_,lowercase_		)["params"]


    def 						snake_case__     (      self :       Any		)->    Dict:





        '''simple docstring'''







        A__          =   self.block_out_channels
        A__          =   block_out_channels[0] * 4

        if self.num_attention_heads is not None:
            raise ValueError(
                'At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.'		)

        # If `num_attention_heads` is not defined (which is the case for most models)
        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
        # The reason for this behavior is to correct for incorrectly named variables that were introduced
        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
        # which is why we correct for the naming here.
        A__          =   self.num_attention_heads or self.attention_head_dim

        # input
        A__          =   nn.Conv(
            block_out_channels[0],kernel_size=(3, 3),strides=(1, 1),padding=((1, 1), (1, 1)),dtype=self.dtype,)

        # time
        A__          =   FlaxTimesteps(
            block_out_channels[0],flip_sin_to_cos=self.flip_sin_to_cos,freq_shift=self.config.freq_shift		)
        A__          =   FlaxTimestepEmbedding(lowercase_,dtype=self.dtype		)

        A__          =   self.only_cross_attention
        if isinstance(lowercase_,lowercase_		):
            A__          =   (only_cross_attention,) * len(self.down_block_types		)

        if isinstance(lowercase_,lowercase_		):
            A__          =   (num_attention_heads,) * len(self.down_block_types		)

        # down
        A__          =   []
        A__          =   block_out_channels[0]
        for i, down_block_type in enumerate(self.down_block_types		):
            A__          =   output_channel
            A__          =   block_out_channels[i]
            A__          =   i == len(lowercase_		) - 1

            if down_block_type == "CrossAttnDownBlock2D":
                A__          =   FlaxCrossAttnDownBlockaD(
                    in_channels=lowercase_,out_channels=lowercase_,dropout=self.dropout,num_layers=self.layers_per_block,num_attention_heads=num_attention_heads[i],add_downsample=not is_final_block,use_linear_projection=self.use_linear_projection,only_cross_attention=only_cross_attention[i],use_memory_efficient_attention=self.use_memory_efficient_attention,dtype=self.dtype,)
            else:
                A__          =   FlaxDownBlockaD(
                    in_channels=lowercase_,out_channels=lowercase_,dropout=self.dropout,num_layers=self.layers_per_block,add_downsample=not is_final_block,dtype=self.dtype,)

            down_blocks.append(lowercase_		)
        A__          =   down_blocks

        # mid
        A__          =   FlaxUNetMidBlockaDCrossAttn(
            in_channels=block_out_channels[-1],dropout=self.dropout,num_attention_heads=num_attention_heads[-1],use_linear_projection=self.use_linear_projection,use_memory_efficient_attention=self.use_memory_efficient_attention,dtype=self.dtype,)

        # up
        A__          =   []
        A__          =   list(reversed(lowercase_		)		)
        A__          =   list(reversed(lowercase_		)		)
        A__          =   list(reversed(lowercase_		)		)
        A__          =   reversed_block_out_channels[0]
        for i, up_block_type in enumerate(self.up_block_types		):
            A__          =   output_channel
            A__          =   reversed_block_out_channels[i]
            A__          =   reversed_block_out_channels[min(i + 1,len(lowercase_		) - 1		)]

            A__          =   i == len(lowercase_		) - 1

            if up_block_type == "CrossAttnUpBlock2D":
                A__          =   FlaxCrossAttnUpBlockaD(
                    in_channels=lowercase_,out_channels=lowercase_,prev_output_channel=lowercase_,num_layers=self.layers_per_block + 1,num_attention_heads=reversed_num_attention_heads[i],add_upsample=not is_final_block,dropout=self.dropout,use_linear_projection=self.use_linear_projection,only_cross_attention=only_cross_attention[i],use_memory_efficient_attention=self.use_memory_efficient_attention,dtype=self.dtype,)
            else:
                A__          =   FlaxUpBlockaD(
                    in_channels=lowercase_,out_channels=lowercase_,prev_output_channel=lowercase_,num_layers=self.layers_per_block + 1,add_upsample=not is_final_block,dropout=self.dropout,dtype=self.dtype,)

            up_blocks.append(lowercase_		)
            A__          =   output_channel
        A__          =   up_blocks

        # out
        A__          =   nn.GroupNorm(num_groups=3_2,epsilon=1E-5		)
        A__          =   nn.Conv(
            self.out_channels,kernel_size=(3, 3),strides=(1, 1),padding=((1, 1), (1, 1)),dtype=self.dtype,)


    def __call__(      self :       Any,lowercase_ :       str,lowercase_ :       List[str],lowercase_ :       List[Any],lowercase_ :       Dict=None,lowercase_ :       List[str]=None,lowercase_ :       bool = True,lowercase_ :       bool = False,)->    Union[FlaxUNetaDConditionOutput, Tuple]:





        '''simple docstring'''







        if not isinstance(lowercase_,jnp.ndarray		):
            A__          =   jnp.array([timesteps],dtype=jnp.intaa		)
        elif isinstance(lowercase_,jnp.ndarray		) and len(timesteps.shape		) == 0:
            A__          =   timesteps.astype(dtype=jnp.floataa		)
            A__          =   jnp.expand_dims(lowercase_,0		)

        A__          =   self.time_proj(lowercase_		)
        A__          =   self.time_embedding(lowercase_		)

        # 2. pre-process
        A__          =   jnp.transpose(lowercase_,(0, 2, 3, 1)		)
        A__          =   self.conv_in(lowercase_		)

        # 3. down
        A__          =   (sample,)
        for down_block in self.down_blocks:
            if isinstance(lowercase_,lowercase_		):
                A__      ,						A__          =   down_block(lowercase_,lowercase_,lowercase_,deterministic=not train		)
            else:
                A__      ,						A__          =   down_block(lowercase_,lowercase_,deterministic=not train		)
            down_block_res_samples += res_samples

        if down_block_additional_residuals is not None:
            A__          =   ()

            for down_block_res_sample, down_block_additional_residual in zip(
                lowercase_,lowercase_		):
                down_block_res_sample += down_block_additional_residual
                new_down_block_res_samples += (down_block_res_sample,)

            A__          =   new_down_block_res_samples

        # 4. mid
        A__          =   self.mid_block(lowercase_,lowercase_,lowercase_,deterministic=not train		)

        if mid_block_additional_residual is not None:
            sample += mid_block_additional_residual

        # 5. up
        for up_block in self.up_blocks:
            A__          =   down_block_res_samples[-(self.layers_per_block + 1) :]
            A__          =   down_block_res_samples[: -(self.layers_per_block + 1)]
            if isinstance(lowercase_,lowercase_		):
                A__          =   up_block(
                    lowercase_,temb=lowercase_,encoder_hidden_states=lowercase_,res_hidden_states_tuple=lowercase_,deterministic=not train,)
            else:
                A__          =   up_block(lowercase_,temb=lowercase_,res_hidden_states_tuple=lowercase_,deterministic=not train		)

        # 6. post-process
        A__          =   self.conv_norm_out(lowercase_		)
        A__          =   nn.silu(lowercase_		)
        A__          =   self.conv_out(lowercase_		)
        A__          =   jnp.transpose(lowercase_,(0, 3, 1, 2)		)

        if not return_dict:
            return (sample,)

        return FlaxUNetaDConditionOutput(sample=lowercase_		)




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      Optional[int]:





    '''simple docstring'''






    global f  # a global dp table for knapsack
    if f[i][j] < 0:
        if j < wt[i - 1]:
            A__          =   mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            A__          =   max(
                mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )    ,			mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			j - wt[i - 1]      ) + val[i - 1]    ,			)
        A__          =   val
    return f[i][j]
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Tuple:





    '''simple docstring'''






    A__          =   [[0] * (w + 1) for _ in range(n + 1      )]

    for i in range(1    ,			n + 1      ):
        for w_ in range(1    ,			w + 1      ):
            if wt[i - 1] <= w_:
                A__          =   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]    ,			dp[i - 1][w_]      )
            else:
                A__          =   dp[i - 1][w_]

    return dp[n][w_], dp
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list      )							->      Union[str, Any]:





    '''simple docstring'''






    if not (isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      ) and isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      )):
        raise ValueError(
            'Both the weights and values vectors must be either lists or tuples'      )

    A__          =   len(SCREAMING_SNAKE_CASE__      )
    if num_items != len(SCREAMING_SNAKE_CASE__      ):
        A__          =   (
            'The number of weights must be the same as the number of values.\n'
            f'But got {num_items} weights and {len(SCREAMING_SNAKE_CASE__      )} values'
        )
        raise ValueError(SCREAMING_SNAKE_CASE__      )
    for i in range(SCREAMING_SNAKE_CASE__      ):
        if not isinstance(wt[i]    ,			SCREAMING_SNAKE_CASE__      ):
            A__          =   (
                'All weights must be integers but got weight of '
                f'type {type(wt[i]      )} at index {i}'
            )
            raise TypeError(SCREAMING_SNAKE_CASE__      )

    A__      ,						A__          =   knapsack(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__          =   set()
    _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

    return optimal_val, example_optional_set
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	set      )							->      Optional[int]:





    '''simple docstring'''






    if i > 0 and j > 0:
        if dp[i - 1][j] == dp[i][j]:
            _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            optimal_set.add(SCREAMING_SNAKE_CASE__      )
            _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			i - 1    ,			j - wt[i - 1]    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     lowercase_       		=  [3, 2, 4, 4]
     lowercase_       		=  [4, 3, 2, 3]
     lowercase_       		=  4
     lowercase_       		=  6
     lowercase_       		=  [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     lowercase_		,			lowercase_       		=  knapsack(w, wt, val, n)
     print(optimal_solution)
     print(mf_knapsack(n, wt, val, w))  # switched the n and w

     # testing the dynamic programming problem with example
     # the optimal subset for the above example are items 3 and 4
     lowercase_		,			lowercase_       		=  knapsack_with_example_solution(w, wt, val)
     assert optimal_solution == 8
     assert optimal_subset == {3, 4}
     print("optimal_value = ", optimal_solution)
     print("An optimal subset corresponding to the optimal value", optimal_subset)

