





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin



@flax.struct.dataclass
class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''
 lowercase_		=      None
 lowercase_		=      None
 lowercase_		=      None  # sigma(t_i)

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Dict)	->str:



   '''simple docstring'''



   return cls()



@dataclass
class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      42
 lowercase_		=      42
 lowercase_		=      42



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->List[Any]:



   '''simple docstring'''



   return True

 @register_to_config
 def __init__(self    :       int	,			UpperCAmelCase_    :       float = 0.02	,			UpperCAmelCase_    :       float = 100	,			UpperCAmelCase_    :       float = 1.007	,			UpperCAmelCase_    :       float = 80	,			UpperCAmelCase_    :       float = 0.05	,			UpperCAmelCase_    :       float = 50	,			)	->List[str]:



   '''simple docstring'''



   pass

 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Dict:



   '''simple docstring'''



   return KarrasVeSchedulerState.create()

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			UpperCAmelCase_    :       KarrasVeSchedulerState	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       Tuple = ())	->KarrasVeSchedulerState:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =jnp.arange(0	,			UpperCAmelCase_)[::-1].copy()
   lowerCamelCase__:				Dict        =[
       (
           self.config.sigma_max**2
           * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
       )
       for i in timesteps
   ]

   return state.replace(
       num_inference_steps=UpperCAmelCase_	,			schedule=jnp.array(UpperCAmelCase_	,			dtype=jnp.floataa)	,			timesteps=UpperCAmelCase_	,			)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       KarrasVeSchedulerState	,			UpperCAmelCase_    :       jnp.ndarray	,			UpperCAmelCase_    :       float	,			UpperCAmelCase_    :       random.KeyArray	,			)	->Tuple[jnp.ndarray, float]:



   '''simple docstring'''



   if self.config.s_min <= sigma <= self.config.s_max:
     lowerCamelCase__:				Optional[int]        =min(self.config.s_churn / state.num_inference_steps	,			2**0.5 - 1)
   else:
     lowerCamelCase__:				Dict        =0

   # sample eps ~ N(0, S_noise^2 * I)
   lowerCamelCase__:				Any        =random.split(UpperCAmelCase_	,			num=1)
   lowerCamelCase__:				str        =self.config.s_noise * random.normal(key=UpperCAmelCase_	,			shape=sample.shape)
   lowerCamelCase__:				Optional[int]        =sigma + gamma * sigma
   lowerCamelCase__:				int        =sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

   return sample_hat, sigma_hat

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       KarrasVeSchedulerState	,			UpperCAmelCase_    :       jnp.ndarray	,			UpperCAmelCase_    :       float	,			UpperCAmelCase_    :       float	,			UpperCAmelCase_    :       jnp.ndarray	,			UpperCAmelCase_    :       bool = True	,			)	->Union[FlaxKarrasVeOutput, Tuple]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =sample_hat + sigma_hat * model_output
   lowerCamelCase__:				int        =(sample_hat - pred_original_sample) / sigma_hat
   lowerCamelCase__:				Optional[int]        =sample_hat + (sigma_prev - sigma_hat) * derivative

   if not return_dict:
     return (sample_prev, derivative, state)

   return FlaxKarrasVeOutput(prev_sample=UpperCAmelCase_	,			derivative=UpperCAmelCase_	,			state=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       KarrasVeSchedulerState	,			UpperCAmelCase_    :       jnp.ndarray	,			UpperCAmelCase_    :       float	,			UpperCAmelCase_    :       float	,			UpperCAmelCase_    :       jnp.ndarray	,			UpperCAmelCase_    :       jnp.ndarray	,			UpperCAmelCase_    :       jnp.ndarray	,			UpperCAmelCase_    :       bool = True	,			)	->Union[FlaxKarrasVeOutput, Tuple]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =sample_prev + sigma_prev * model_output
   lowerCamelCase__:				Dict        =(sample_prev - pred_original_sample) / sigma_prev
   lowerCamelCase__:				Optional[Any]        =sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

   if not return_dict:
     return (sample_prev, derivative, state)

   return FlaxKarrasVeOutput(prev_sample=UpperCAmelCase_	,			derivative=UpperCAmelCase_	,			state=UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       KarrasVeSchedulerState	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       Optional[Any])	->Dict:



   '''simple docstring'''



   raise NotImplementedError()









from __future__ import annotations





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		List[Any]:







  """simple docstring"""





  print(F"""Vertex\tShortest Distance from vertex {src}"""		)
  for i, d in enumerate(__a		):
    print(F"""{i}\t\t{d}"""		)





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		Tuple:







  """simple docstring"""





  for j in range(__a		):
    lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				str        =(graph[j][k] for k in ["src", "dst", "weight"])
    if distance[u] != float("inf"		) and distance[u] + w < distance[v]:
      return True
  return False





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a		)					->		list[float]:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =[float("inf"		)] * vertex_count
  lowerCamelCase__:				List[str]        =0.0

  for _ in range(vertex_count - 1		):
    for j in range(__a		):
      lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Union[str, Any]        =(graph[j][k] for k in ["src", "dst", "weight"])

      if distance[u] != float("inf"		) and distance[u] + w < distance[v]:
        lowerCamelCase__:				int        =distance[u] + w

  lowerCamelCase__:				Tuple        =check_negative_cycle(__a     ,   __a     ,   __a		)
  if negative_cycle_exists:
    raise Exception("Negative cycle found"		)

  return distance


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     __A     =    int(input("Enter number of vertices: ").strip())
     __A     =    int(input("Enter number of edges: ").strip())

     __A     =    [{} for _ in range(E)]

     for i in range(E):
          print("Edge ", i + 1)
          __A ,     __A ,     __A     =    (
              int(x)
              for x in input("Enter source, destination, weight: ").strip().split(" ")
          )
          __A     =    {"src": src, "dst": dest, "weight": weight}

     __A     =    int(input("\nEnter shortest path source:").strip())
     __A     =    bellman_ford(graph, V, E, source)
     print_distance(shortest_distance, 0)

