






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


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		List[Any]					='''linear'''
       __lowerCAmelCase    :		Any					='''cosine'''
       __lowerCAmelCase    :		Any					='''cosine_with_restarts'''
       __lowerCAmelCase    :		List[str]					='''polynomial'''
       __lowerCAmelCase    :		Union[str, Any]					='''constant'''
       __lowerCAmelCase    :		List[Any]					='''constant_with_warmup'''
       __lowerCAmelCase    :		Union[str, Any]					='''piecewise_constant'''





def        _snake_case   (_snake_case				:       Optimizer      ,    _snake_case				:       int = -1)					->	int:
   return LambdaLR(_snake_case      ,    lambda _snake_case: 1      ,    last_epoch=_snake_case)





def        _snake_case   (_snake_case				:       Optimizer      ,    _snake_case				:       int      ,    _snake_case				:       int = -1)					->	str:

   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        _snake_case   (_snake_case				:       Optimizer      ,    _snake_case				:       str      ,    _snake_case				:       int = -1)					->	int:
   _lowercase              ={}
   _lowercase              =step_rules.split(',')
   for rule_str in rule_list[:-1]:
      _lowercase       ,       _lowercase              =rule_str.split(':')
      _lowercase              =int(_snake_case)
      _lowercase              =float(_snake_case)
      _lowercase              =value
   _lowercase              =float(rule_list[-1])

   def create_rules_function(_snake_case				:       Any      ,    _snake_case				:       Any):
      def rule_func(_snake_case				:       int) -> float:
         _lowercase              =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

   _lowercase              =create_rules_function(_snake_case      ,    _snake_case)

   return LambdaLR(_snake_case      ,    _snake_case      ,    last_epoch=_snake_case)





def        _snake_case   (_snake_case				:       List[str]      ,    _snake_case				:       List[Any]      ,    _snake_case				:       Optional[int]      ,    _snake_case				:       List[Any]=-1)					->	Dict:

   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        _snake_case   (_snake_case				:       Optimizer      ,    _snake_case				:       int      ,    _snake_case				:       int      ,    _snake_case				:       float = 0.5      ,    _snake_case				:       int = -1)					->	Any:

   def lr_lambda(_snake_case				:       Optional[int]):
      if current_step < num_warmup_steps:
         return float(_snake_case) / float(max(1      ,    _snake_case))
      _lowercase              =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        _snake_case   (_snake_case				:       Optimizer      ,    _snake_case				:       int      ,    _snake_case				:       int      ,    _snake_case				:       int = 1      ,    _snake_case				:       int = -1)					->	List[Any]:

   def lr_lambda(_snake_case				:       int):
      if current_step < num_warmup_steps:
         return float(_snake_case) / float(max(1      ,    _snake_case))
      _lowercase              =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        _snake_case   (_snake_case				:       List[Any]      ,    _snake_case				:       List[Any]      ,    _snake_case				:       Any      ,    _snake_case				:       List[str]=1E-7      ,    _snake_case				:       Tuple=1.0      ,    _snake_case				:       List[str]=-1)					->	List[str]:
   _lowercase              =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:
         _lowercase              =lr_init - lr_end
         _lowercase              =num_training_steps - num_warmup_steps
         _lowercase              =1 - (current_step - num_warmup_steps) / decay_steps
         _lowercase              =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)


_SCREAMING_SNAKE_CASE   =  {
    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        _snake_case   (_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      ,    )					->	Dict:
   _lowercase              =SchedulerType(_snake_case)
   _lowercase              =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)










class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __init__(				self	:List[Any]):



          """simple docstring"""

          _lowercase              ={}





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          print(self.vertex)
          for i in self.vertex:
             print(snake_case,      ' -> ',      ' -> '.join([str(snake_case) for j in self.vertex[i]]))





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:int,      snake_case	:int):



          """simple docstring"""

          if from_vertex in self.vertex:
             self.vertex[from_vertex].append(snake_case)
          else:
             # else make a new vertex
             _lowercase              =[to_vertex]





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          _lowercase              =[False] * len(self.vertex)

          # call the recursive helper function
          for i in range(len(self.vertex)):
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:list):



          """simple docstring"""

          _lowercase              =True

          print(snake_case,      end=' ')

          # Recur for all the vertices that are adjacent to this node
          for i in self.vertex:
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE   =  Graph()
    g.add_edge(0, 1)
    g.add_edge(0, 2)
    g.add_edge(1, 2)
    g.add_edge(2, 0)
    g.add_edge(2, 3)
    g.add_edge(3, 3)

    g.print_graph()
    print("DFS:")
    g.dfs()

    # OUTPUT:
    # 0  ->  1 -> 2
    # 1  ->  2
    # 2  ->  0 -> 3
    # 3  ->  3
    # DFS:
    #  0 1 2 3


