



import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


__UpperCAmelCase          =       logging.get_logger(__name__)



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					List[Any]        =  "linear"
       UpperCAmelCase__				:					List[Any]        =  "cosine"
       UpperCAmelCase__				:					List[Any]        =  "cosine_with_restarts"
       UpperCAmelCase__				:					Dict        =  "polynomial"
       UpperCAmelCase__				:					Optional[Any]        =  "constant"
       UpperCAmelCase__				:					Any        =  "constant_with_warmup"
       UpperCAmelCase__				:					List[Any]        =  "piecewise_constant"






def 		UpperCamelCase	(   snake_case__       :    Optimizer       ,     snake_case__       :    int = -1	)	->     Tuple:
   return LambdaLR(snake_case__       ,     lambda snake_case__	: 1       ,     last_epoch=snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    Optimizer       ,     snake_case__       :    int       ,     snake_case__       :    int = -1	)	->     int:

   def lr_lambda(snake_case__       :    int	):
      if current_step < num_warmup_steps:
         return float(snake_case__	) / float(max(1.0       ,     snake_case__	)	)
      return 1.0

   return LambdaLR(snake_case__       ,     snake_case__       ,     last_epoch=snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    Optimizer       ,     snake_case__       :    str       ,     snake_case__       :    int = -1	)	->     Optional[Any]:
   UpperCamelCase    :  List[Any]							=	{}
   UpperCamelCase    :  Optional[Any]							=	step_rules.split(','	)
   for rule_str in rule_list[:-1]:
      UpperCamelCase	,     UpperCamelCase    :  Optional[int]							=	rule_str.split(':'	)
      UpperCamelCase    :  Dict							=	int(snake_case__	)
      UpperCamelCase    :  Optional[int]							=	float(snake_case__	)
      UpperCamelCase    :  Union[str, Any]							=	value
   UpperCamelCase    :  Dict							=	float(rule_list[-1]	)

   def create_rules_function(snake_case__       :    Union[str, Any]       ,     snake_case__       :    Union[str, Any]	):
      def rule_func(snake_case__       :    int	) -> float:
         UpperCamelCase    :  str							=	sorted(rules_dict.keys()	)
         for i, sorted_step in enumerate(snake_case__	):
            if steps < sorted_step:
               return rules_dict[sorted_steps[i]]
         return last_lr_multiple

      return rule_func

   UpperCamelCase    :  Dict							=	create_rules_function(snake_case__       ,     snake_case__	)

   return LambdaLR(snake_case__       ,     snake_case__       ,     last_epoch=snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    Dict       ,     snake_case__       :    int       ,     snake_case__       :    Tuple       ,     snake_case__       :    Dict=-1	)	->     List[str]:

   def lr_lambda(snake_case__       :    int	):
      if current_step < num_warmup_steps:
         return float(snake_case__	) / float(max(1       ,     snake_case__	)	)
      return max(
          0.0       ,     float(num_training_steps - current_step	) / float(max(1       ,     num_training_steps - num_warmup_steps	)	)	)

   return LambdaLR(snake_case__       ,     snake_case__       ,     snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    Optimizer       ,     snake_case__       :    int       ,     snake_case__       :    int       ,     snake_case__       :    float = 0.5       ,     snake_case__       :    int = -1	)	->     Any:

   def lr_lambda(snake_case__       :    str	):
      if current_step < num_warmup_steps:
         return float(snake_case__	) / float(max(1       ,     snake_case__	)	)
      UpperCamelCase    :  Any							=	float(current_step - num_warmup_steps	) / float(max(1       ,     num_training_steps - num_warmup_steps	)	)
      return max(0.0       ,     0.5 * (1.0 + math.cos(math.pi * float(snake_case__	) * 2.0 * progress	))	)

   return LambdaLR(snake_case__       ,     snake_case__       ,     snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    Optimizer       ,     snake_case__       :    int       ,     snake_case__       :    int       ,     snake_case__       :    int = 1       ,     snake_case__       :    int = -1	)	->     Optional[Any]:

   def lr_lambda(snake_case__       :    int	):
      if current_step < num_warmup_steps:
         return float(snake_case__	) / float(max(1       ,     snake_case__	)	)
      UpperCamelCase    :  Any							=	float(current_step - num_warmup_steps	) / float(max(1       ,     num_training_steps - num_warmup_steps	)	)
      if progress >= 1.0:
         return 0.0
      return max(0.0       ,     0.5 * (1.0 + math.cos(math.pi * ((float(snake_case__	) * progress) % 1.0)	))	)

   return LambdaLR(snake_case__       ,     snake_case__       ,     snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    Any       ,     snake_case__       :    Any       ,     snake_case__       :    str       ,     snake_case__       :    Union[str, Any]=1E-7       ,     snake_case__       :    Optional[int]=1.0       ,     snake_case__       :    Optional[int]=-1	)	->     List[str]:
   UpperCamelCase    :  List[str]							=	optimizer.defaults['lr']
   if not (lr_init > lr_end):
      raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""	)

   def lr_lambda(snake_case__       :    int	):
      if current_step < num_warmup_steps:
         return float(snake_case__	) / float(max(1       ,     snake_case__	)	)
      elif current_step > num_training_steps:
         return lr_end / lr_init  # as LambdaLR multiplies by lr_init
      else:
         UpperCamelCase    :  int							=	lr_init - lr_end
         UpperCamelCase    :  Tuple							=	num_training_steps - num_warmup_steps
         UpperCamelCase    :  int							=	1 - (current_step - num_warmup_steps) / decay_steps
         UpperCamelCase    :  str							=	lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(snake_case__       ,     snake_case__       ,     snake_case__	)


__UpperCAmelCase          =       {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}


def 		UpperCamelCase	(   snake_case__       :    Union[str, SchedulerType]       ,     snake_case__       :    Optimizer       ,     snake_case__       :    Optional[str] = None       ,     snake_case__       :    Optional[int] = None       ,     snake_case__       :    Optional[int] = None       ,     snake_case__       :    int = 1       ,     snake_case__       :    float = 1.0       ,     snake_case__       :    int = -1       ,     )	->     Union[str, Any]:
   UpperCamelCase    :  Union[str, Any]							=	SchedulerType(snake_case__	)
   UpperCamelCase    :  Optional[int]							=	TYPE_TO_SCHEDULER_FUNCTION[name]
   if name == SchedulerType.CONSTANT:
      return schedule_func(snake_case__       ,     last_epoch=snake_case__	)

   if name == SchedulerType.PIECEWISE_CONSTANT:
      return schedule_func(snake_case__       ,     step_rules=snake_case__       ,     last_epoch=snake_case__	)

   # All other schedulers require `num_warmup_steps`
   if num_warmup_steps is None:
      raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument."""	)

   if name == SchedulerType.CONSTANT_WITH_WARMUP:
      return schedule_func(snake_case__       ,     num_warmup_steps=snake_case__       ,     last_epoch=snake_case__	)

   # All other schedulers require `num_training_steps`
   if num_training_steps is None:
      raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument."""	)

   if name == SchedulerType.COSINE_WITH_RESTARTS:
      return schedule_func(
          snake_case__       ,     num_warmup_steps=snake_case__       ,     num_training_steps=snake_case__       ,     num_cycles=snake_case__       ,     last_epoch=snake_case__       ,     )

   if name == SchedulerType.POLYNOMIAL:
      return schedule_func(
          snake_case__       ,     num_warmup_steps=snake_case__       ,     num_training_steps=snake_case__       ,     power=snake_case__       ,     last_epoch=snake_case__       ,     )

   return schedule_func(
       snake_case__       ,     num_warmup_steps=snake_case__       ,     num_training_steps=snake_case__       ,     last_epoch=snake_case__	)








from __future__ import annotations

from typing import Any



class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_       )     ->		None:
          UpperCamelCase    :  Any							=	num_of_nodes
          UpperCamelCase    :  list[list[int]]							=	[]
          UpperCamelCase    :  dict[int, int]							=	{}
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		None:
          self.m_edges.append([u_node, v_node, weight]       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		int:

          if self.m_component[u_node] == u_node:
             return u_node
          return self.find_component(self.m_component[u_node]       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		None:

          if self.m_component[u_node] != u_node:
             for k in self.m_component:
                UpperCamelCase    :  Dict							=	self.find_component(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		None:

          if component_size[u_node] <= component_size[v_node]:
             UpperCamelCase    :  Tuple							=	v_node
             component_size[v_node] += component_size[u_node]
             self.set_component(SCREAMING_SNAKE_CASE_       )

          elif component_size[u_node] >= component_size[v_node]:
             UpperCamelCase    :  Union[str, Any]							=	self.find_component(SCREAMING_SNAKE_CASE_       )
             component_size[u_node] += component_size[v_node]
             self.set_component(SCREAMING_SNAKE_CASE_       )






       def 					snake_case_       (			self       )     ->		None:
          UpperCamelCase    :  int							=	[]
          UpperCamelCase    :  int							=	0

          UpperCamelCase    :  list[Any]							=	[-1] * self.m_num_of_nodes

          # A list of components (initialized to all of the nodes)
          for node in range(self.m_num_of_nodes       ):
             self.m_component.update({node: node}       )
             component_size.append(1       )

          UpperCamelCase    :  List[str]							=	self.m_num_of_nodes

          while num_of_components > 1:
             for edge in self.m_edges:
                UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  List[Any]							=	edge

                UpperCamelCase    :  str							=	self.m_component[u]
                UpperCamelCase    :  Any							=	self.m_component[v]

                if u_component != v_component:

                   for component in (u_component, v_component):
                      if (
                          minimum_weight_edge[component] == -1
                          or minimum_weight_edge[component][2] > w
                      ):
                         UpperCamelCase    :  Any							=	[u, v, w]

             for edge in minimum_weight_edge:
                if isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
                   UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  int							=	edge

                   UpperCamelCase    :  List[Any]							=	self.m_component[u]
                   UpperCamelCase    :  Tuple							=	self.m_component[v]

                   if u_component != v_component:
                      mst_weight += w
                      self.union(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
                      print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""       )
                      num_of_components -= 1

             UpperCamelCase    :  Optional[Any]							=	[-1] * self.m_num_of_nodes
          print(F"""The total weight of the minimal spanning tree is: {mst_weight}"""       )






def 		UpperCamelCase	(   )	->     None:
   pass


if __name__ == "__main__":
    import doctest

    doctest.testmod()



