"""simple docstring"""




import flax.linen as nn
import jax
import jax.numpy as jnp
class 						__A							( nn.Module		):
     _UpperCamelCase							:			int
     _UpperCamelCase							:			jnp.dtype							=		jnp.floataa






     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     nn.Conv(
           self.out_channels				,     kernel_size=(3, 3)				,     strides=(1, 1)				,     padding=((1, 1), (1, 1))				,     dtype=self.dtype				,     )






     def __call__(		self				,     a__  ):
       _lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase						:       Tuple  			=     hidden_states.shape
       _lowerCAmelCase						:       List[Any]  			=     jax.image.resize(
           a__				,     shape=(batch, height * 2, width * 2, channels)				,     method="""nearest"""				,     )
       _lowerCAmelCase						:       str  			=     self.conv(a__  )
       return hidden_states







class 						__A							( nn.Module		):
     _UpperCamelCase							:			int
     _UpperCamelCase							:			jnp.dtype							=		jnp.floataa






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     nn.Conv(
           self.out_channels				,     kernel_size=(3, 3)				,     strides=(2, 2)				,     padding=((1, 1), (1, 1))				,     dtype=self.dtype				,     )






     def __call__(		self				,     a__  ):
       # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
       # hidden_states = jnp.pad(hidden_states, pad_width=pad)
       _lowerCAmelCase						:       Optional[int]  			=     self.conv(a__  )
       return hidden_states







class 						__A							( nn.Module		):
     _UpperCamelCase							:			int
     _UpperCamelCase							:			int							=		None
     _UpperCamelCase							:			float							=		0.0
     _UpperCamelCase							:			bool							=		None
     _UpperCamelCase							:			jnp.dtype							=		jnp.floataa






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.in_channels if self.out_channels is None else self.out_channels

       _lowerCAmelCase						:       List[str]  			=     nn.GroupNorm(num_groups=32				,     epsilon=1e-5  )
       _lowerCAmelCase						:       Tuple  			=     nn.Conv(
           a__				,     kernel_size=(3, 3)				,     strides=(1, 1)				,     padding=((1, 1), (1, 1))				,     dtype=self.dtype				,     )

       _lowerCAmelCase						:       Optional[Any]  			=     nn.Dense(a__				,     dtype=self.dtype  )

       _lowerCAmelCase						:       Any  			=     nn.GroupNorm(num_groups=32				,     epsilon=1e-5  )
       _lowerCAmelCase						:       Any  			=     nn.Dropout(self.dropout_prob  )
       _lowerCAmelCase						:       Optional[Any]  			=     nn.Conv(
           a__				,     kernel_size=(3, 3)				,     strides=(1, 1)				,     padding=((1, 1), (1, 1))				,     dtype=self.dtype				,     )

       _lowerCAmelCase						:       Any  			=     self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

       _lowerCAmelCase						:       str  			=     None
       if use_nin_shortcut:
         _lowerCAmelCase						:       Dict  			=     nn.Conv(
             a__				,     kernel_size=(1, 1)				,     strides=(1, 1)				,     padding="""VALID"""				,     dtype=self.dtype				,     )






     def __call__(		self				,     a__				,     a__				,     a__=True  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     hidden_states
       _lowerCAmelCase						:       Union[str, Any]  			=     self.norma(a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     nn.swish(a__  )
       _lowerCAmelCase						:       Dict  			=     self.conva(a__  )

       _lowerCAmelCase						:       Any  			=     self.time_emb_proj(nn.swish(a__  )  )
       _lowerCAmelCase						:       int  			=     jnp.expand_dims(jnp.expand_dims(a__				,     1  )				,     1  )
       _lowerCAmelCase						:       Optional[Any]  			=     hidden_states + temb

       _lowerCAmelCase						:       Optional[Any]  			=     self.norma(a__  )
       _lowerCAmelCase						:       int  			=     nn.swish(a__  )
       _lowerCAmelCase						:       Optional[int]  			=     self.dropout(a__				,     a__  )
       _lowerCAmelCase						:       List[Any]  			=     self.conva(a__  )

       if self.conv_shortcut is not None:
         _lowerCAmelCase						:       Tuple  			=     self.conv_shortcut(a__  )

       return hidden_states + residual









"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCAmelCase    :Tuple     =					tuple[int, int]
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A		)							->					None:
         _UpperCAmelCase							:  set[int]											=    vertices
         _UpperCAmelCase							:  dict[EdgeT, int]											=    {
             (min(A		), max(A		)): weight for edge, weight in edges.items()
         }





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					None:
         self.vertices.add(edge[0]		)
         self.vertices.add(edge[1]		)
         _UpperCAmelCase							:  List[Any]											=    weight





     def  __lowerCAmelCase	(					self		)							->					Graph:
         _UpperCAmelCase							:  Graph											=    Graph({min(self.vertices		)}       ,	{}		)
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int

         while len(subgraph.vertices		) < len(self.vertices		):
             _UpperCAmelCase							:  Any											=    max(self.edges.values()		) + 1
             for edge, weight in self.edges.items():
                 if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                     if weight < min_weight:
                         _UpperCAmelCase							:  Tuple											=    edge
                         _UpperCAmelCase							:  Optional[int]											=    weight

             subgraph.add_edge(A       ,	A		)

         return subgraph





def  lowerCamelCase_						(UpperCamelCase__		:		str = "p107_network.txt"							):
    _UpperCAmelCase							:  str											=    os.path.abspath(os.path.dirname(UpperCamelCase__							)							)
    _UpperCAmelCase							:  str											=    os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)
    _UpperCAmelCase							:  dict[EdgeT, int]											=    {}
    _UpperCAmelCase							:  list[str]
    _UpperCAmelCase							:  int
    _UpperCAmelCase							:  int

    with open(UpperCamelCase__							) as f:
        _UpperCAmelCase							:  str											=    f.read().strip().split('''\n'''							)

    _UpperCAmelCase							:  List[Any]											=    [line.split(''','''							) for line in data]

    for edgea in range(1     ,       len(UpperCamelCase__							)							):
        for edgea in range(UpperCamelCase__							):
            if adjaceny_matrix[edgea][edgea] != "-":
                _UpperCAmelCase							:  Optional[Any]											=    int(adjaceny_matrix[edgea][edgea]							)

    _UpperCAmelCase							:  Graph											=    Graph(set(range(len(UpperCamelCase__							)							)							)     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Graph											=    graph.prims_algorithm()

    _UpperCAmelCase							:  int											=    sum(graph.edges.values()							)
    _UpperCAmelCase							:  int											=    sum(subgraph.edges.values()							)

    return initial_total - optimal_total


if __name__ == "__main__":
  print(f"{solution() = }")


