


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 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       42







@flax_register_to_config
class 		_snake_case  (      nn.Module						,    _snake_case						,    _snake_case							):
 SCREAMING_SNAKE_CASE__      =       32
 SCREAMING_SNAKE_CASE__      =       4
 SCREAMING_SNAKE_CASE__      =       4
 SCREAMING_SNAKE_CASE__      =       (
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "DownBlock2D",
 )
 SCREAMING_SNAKE_CASE__      =       ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
 SCREAMING_SNAKE_CASE__      =       False
 SCREAMING_SNAKE_CASE__      =       (320, 640, 1280, 1280)
 SCREAMING_SNAKE_CASE__      =       2
 SCREAMING_SNAKE_CASE__      =       8
 SCREAMING_SNAKE_CASE__      =       None
 SCREAMING_SNAKE_CASE__      =       1280
 SCREAMING_SNAKE_CASE__      =       0.0
 SCREAMING_SNAKE_CASE__      =       False
 SCREAMING_SNAKE_CASE__      =       jnp.floataa
 SCREAMING_SNAKE_CASE__      =       True
 SCREAMING_SNAKE_CASE__      =       0
 SCREAMING_SNAKE_CASE__      =       False




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       # init input tensors
       a						:str 							=       (1, self.in_channels, self.sample_size, self.sample_size)
       a						:Tuple 							=       jnp.zeros(_lowerCamelCase					,  dtype=jnp.floataa    )
       a						:List[str] 							=       jnp.ones((1,)					,  dtype=jnp.intaa    )
       a						:Dict 							=       jnp.zeros((1, 1, self.cross_attention_dim)					,  dtype=jnp.floataa    )

       a							,				a						:List[str] 							=       jax.random.split(_lowerCamelCase    )
       a						:Dict 							=       {'''params''': params_rng, '''dropout''': dropout_rng}

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




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       self.block_out_channels
       a						:Any 							=       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						:Optional[int] 							=       self.num_attention_heads or self.attention_head_dim

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

       # time
       a						:int 							=       FlaxTimesteps(
           block_out_channels[0]					,  flip_sin_to_cos=self.flip_sin_to_cos					,  freq_shift=self.config.freq_shift    )
       a						:Optional[int] 							=       FlaxTimestepEmbedding(_lowerCamelCase					,  dtype=self.dtype    )

       a						:Tuple 							=       self.only_cross_attention
       if isinstance(_lowerCamelCase					,  _lowerCamelCase    ):
             a						:Optional[Any] 							=       (only_cross_attention,) * len(self.down_block_types    )

       if isinstance(_lowerCamelCase					,  _lowerCamelCase    ):
             a						:Tuple 							=       (num_attention_heads,) * len(self.down_block_types    )

       # down
       a						:Optional[Any] 							=       []
       a						:Union[str, Any] 							=       block_out_channels[0]
       for i, down_block_type in enumerate(self.down_block_types    ):
             a						:Optional[Any] 							=       output_channel
             a						:Any 							=       block_out_channels[i]
             a						:Any 							=       i == len(_lowerCamelCase    ) - 1

             if down_block_type == "CrossAttnDownBlock2D":
                   a						:Union[str, Any] 							=       FlaxCrossAttnDownBlockaD(
                       in_channels=_lowerCamelCase					,  out_channels=_lowerCamelCase					,  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						:str 							=       FlaxDownBlockaD(
                       in_channels=_lowerCamelCase					,  out_channels=_lowerCamelCase					,  dropout=self.dropout					,  num_layers=self.layers_per_block					,  add_downsample=not is_final_block					,  dtype=self.dtype					,  )

             down_blocks.append(_lowerCamelCase    )
       a						:Any 							=       down_blocks

       # mid
       a						:Any 							=       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						:List[str] 							=       []
       a						:List[Any] 							=       list(reversed(_lowerCamelCase    )    )
       a						:List[Any] 							=       list(reversed(_lowerCamelCase    )    )
       a						:str 							=       list(reversed(_lowerCamelCase    )    )
       a						:List[str] 							=       reversed_block_out_channels[0]
       for i, up_block_type in enumerate(self.up_block_types    ):
             a						:Union[str, Any] 							=       output_channel
             a						:Tuple 							=       reversed_block_out_channels[i]
             a						:str 							=       reversed_block_out_channels[min(i + 1					,  len(_lowerCamelCase    ) - 1    )]

             a						:str 							=       i == len(_lowerCamelCase    ) - 1

             if up_block_type == "CrossAttnUpBlock2D":
                   a						:Tuple 							=       FlaxCrossAttnUpBlockaD(
                       in_channels=_lowerCamelCase					,  out_channels=_lowerCamelCase					,  prev_output_channel=_lowerCamelCase					,  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						:str 							=       FlaxUpBlockaD(
                       in_channels=_lowerCamelCase					,  out_channels=_lowerCamelCase					,  prev_output_channel=_lowerCamelCase					,  num_layers=self.layers_per_block + 1					,  add_upsample=not is_final_block					,  dropout=self.dropout					,  dtype=self.dtype					,  )

             up_blocks.append(_lowerCamelCase    )
             a						:Union[str, Any] 							=       output_channel
       a						:Tuple 							=       up_blocks

       # out
       a						:Any 							=       nn.GroupNorm(num_groups=32					,  epsilon=1e-5    )
       a						:List[Any] 							=       nn.Conv(
           self.out_channels					,  kernel_size=(3, 3)					,  strides=(1, 1)					,  padding=((1, 1), (1, 1))					,  dtype=self.dtype					,  )




 def __call__(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase = True					,  _lowerCamelCase = False					,  ):
       # 1. time
       if not isinstance(_lowerCamelCase					,  jnp.ndarray    ):
             a						:Union[str, Any] 							=       jnp.array([timesteps]					,  dtype=jnp.intaa    )
       elif isinstance(_lowerCamelCase					,  jnp.ndarray    ) and len(timesteps.shape    ) == 0:
             a						:Optional[Any] 							=       timesteps.astype(dtype=jnp.floataa    )
             a						:Tuple 							=       jnp.expand_dims(_lowerCamelCase					,  0    )

       a						:Tuple 							=       self.time_proj(_lowerCamelCase    )
       a						:str 							=       self.time_embedding(_lowerCamelCase    )

       # 2. pre-process
       a						:Optional[Any] 							=       jnp.transpose(_lowerCamelCase					,  (0, 2, 3, 1)    )
       a						:int 							=       self.conv_in(_lowerCamelCase    )

       # 3. down
       a						:List[str] 							=       (sample,)
       for down_block in self.down_blocks:
             if isinstance(_lowerCamelCase					,  _lowerCamelCase    ):
                   a							,				a						:Optional[Any] 							=       down_block(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  deterministic=not train    )
             else:
                   a							,				a						:Dict 							=       down_block(_lowerCamelCase					,  _lowerCamelCase					,  deterministic=not train    )
             down_block_res_samples += res_samples

       if down_block_additional_residuals is not None:
             a						:Any 							=       ()

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

             a						:Optional[Any] 							=       new_down_block_res_samples

       # 4. mid
       a						:Tuple 							=       self.mid_block(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  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						:int 							=       down_block_res_samples[-(self.layers_per_block + 1) :]
             a						:int 							=       down_block_res_samples[: -(self.layers_per_block + 1)]
             if isinstance(_lowerCamelCase					,  _lowerCamelCase    ):
                   a						:Dict 							=       up_block(
                       _lowerCamelCase					,  temb=_lowerCamelCase					,  encoder_hidden_states=_lowerCamelCase					,  res_hidden_states_tuple=_lowerCamelCase					,  deterministic=not train					,  )
             else:
                   a						:Optional[int] 							=       up_block(_lowerCamelCase					,  temb=_lowerCamelCase					,  res_hidden_states_tuple=_lowerCamelCase					,  deterministic=not train    )

        # 6. post-process
       a						:Tuple 							=       self.conv_norm_out(_lowerCamelCase    )
       a						:Union[str, Any] 							=       nn.silu(_lowerCamelCase    )
       a						:List[Any] 							=       self.conv_out(_lowerCamelCase    )
       a						:str 							=       jnp.transpose(_lowerCamelCase					,  (0, 3, 1, 2)    )

       if not return_dict:
             return (sample,)

       return FlaxUNetaDConditionOutput(sample=_lowerCamelCase    )









import math







class 		_snake_case  :




 def __init__(		self					,  _lowerCamelCase=0    ):  # a graph with Node 0,1,...,N-1
       a						:Optional[int] 							=       n
       a						:Union[str, Any] 							=       [
           [math.inf for j in range(0					,  _lowerCamelCase    )] for i in range(0					,  _lowerCamelCase    )
       ]  # adjacency matrix for weight
       a						:List[Any] 							=       [
           [math.inf for j in range(0					,  _lowerCamelCase    )] for i in range(0					,  _lowerCamelCase    )
       ]  # dp[i][j] stores minimum distance from i to j




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:Tuple 							=       w




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for k in range(0					,  self.n    ):
             for i in range(0					,  self.n    ):
                   for j in range(0					,  self.n    ):
                         a						:Union[str, Any] 							=       min(self.dp[i][j]					,  self.dp[i][k] + self.dp[k][j]    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       return self.dp[u][v]


if __name__ == "__main__":
     snake_case				:    str       				=     Graph(5)
     graph.add_edge(0, 2, 9)
     graph.add_edge(0, 4, 10)
     graph.add_edge(1, 3, 5)
     graph.add_edge(2, 3, 7)
     graph.add_edge(3, 0, 10)
     graph.add_edge(3, 1, 2)
     graph.add_edge(3, 2, 1)
     graph.add_edge(3, 4, 6)
     graph.add_edge(4, 1, 3)
     graph.add_edge(4, 2, 4)
     graph.add_edge(4, 3, 9)
     graph.floyd_warshall()
     graph.show_min(1, 4)
     graph.show_min(0, 3)





