'''simple docstring'''

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


__a:			Any	       =       logging.get_logger(__name__)



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   "linear"
       SCREAMING_SNAKE_CASE 		=   "cosine"
       SCREAMING_SNAKE_CASE 		=   "cosine_with_restarts"
       SCREAMING_SNAKE_CASE 		=   "polynomial"
       SCREAMING_SNAKE_CASE 		=   "constant"
       SCREAMING_SNAKE_CASE 		=   "constant_with_warmup"
       SCREAMING_SNAKE_CASE 		=   "piecewise_constant"


def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase = -1					):
 return LambdaLR(_lowerCamelCase		,					lambda UpperCAmelCase					: 1		,					last_epoch=_lowerCamelCase					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = -1					):

 def lr_lambda(UpperCAmelCase					):
  if current_step < num_warmup_steps:
   return float(_lowerCamelCase					) / float(max(1.0		,					_lowerCamelCase					)					)
  return 1.0

 return LambdaLR(_lowerCamelCase		,					_lowerCamelCase		,					last_epoch=_lowerCamelCase					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = -1					):
 lowercase__    :   List[Any]						= {}
 lowercase__    :   str						= step_rules.split(''','''					)
 for rule_str in rule_list[:-1]:
  lowercase__    :   int						= rule_str.split(''':'''					)
  lowercase__    :   Union[str, Any]						= int(_lowerCamelCase					)
  lowercase__    :   List[str]						= float(_lowerCamelCase					)
  lowercase__    :   Union[str, Any]						= value
 lowercase__    :   Any						= float(rule_list[-1]					)

 def create_rules_function(UpperCAmelCase		,					UpperCAmelCase					):
  def rule_func(UpperCAmelCase					) -> float:
   lowercase__    :   Optional[int]						= sorted(rules_dict.keys()					)
   for i, sorted_step in enumerate(_lowerCamelCase					):
    if steps < sorted_step:
     return rules_dict[sorted_steps[i]]
   return last_lr_multiple

  return rule_func

 lowercase__    :   List[str]						= create_rules_function(_lowerCamelCase		,					_lowerCamelCase					)

 return LambdaLR(_lowerCamelCase		,					_lowerCamelCase		,					last_epoch=_lowerCamelCase					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=-1					):

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

 return LambdaLR(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = 0.5		,					UpperCAmelCase = -1					):

 def lr_lambda(UpperCAmelCase					):
  if current_step < num_warmup_steps:
   return float(_lowerCamelCase					) / float(max(1		,					_lowerCamelCase					)					)
  lowercase__    :   List[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(_lowerCamelCase					) * 2.0 * progress					))					)

 return LambdaLR(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = 1		,					UpperCAmelCase = -1					):

 def lr_lambda(UpperCAmelCase					):
  if current_step < num_warmup_steps:
   return float(_lowerCamelCase					) / float(max(1		,					_lowerCamelCase					)					)
  lowercase__    :   Tuple						= 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(_lowerCamelCase					) * progress) % 1.0)					))					)

 return LambdaLR(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=1E-7		,					UpperCAmelCase=1.0		,					UpperCAmelCase=-1					):
 lowercase__    :   int						= 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(UpperCAmelCase					):
  if current_step < num_warmup_steps:
   return float(_lowerCamelCase					) / float(max(1		,					_lowerCamelCase					)					)
  elif current_step > num_training_steps:
   return lr_end / lr_init  # as LambdaLR multiplies by lr_init
  else:
   lowercase__    :   Dict						= lr_init - lr_end
   lowercase__    :   Optional[Any]						= num_training_steps - num_warmup_steps
   lowercase__    :   Tuple						= 1 - (current_step - num_warmup_steps) / decay_steps
   lowercase__    :   Tuple						= lr_range * pct_remaining**power + lr_end
   return decay / lr_init  # as LambdaLR multiplies by lr_init

 return LambdaLR(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase					)


__a:			List[str]	       =       {
    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	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = 1		,					UpperCAmelCase = 1.0		,					UpperCAmelCase = -1		,					):
 lowercase__    :   Optional[int]						= SchedulerType(_lowerCamelCase					)
 lowercase__    :   Any						= TYPE_TO_SCHEDULER_FUNCTION[name]
 if name == SchedulerType.CONSTANT:
  return schedule_func(_lowerCamelCase		,					last_epoch=_lowerCamelCase					)

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

 # 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(_lowerCamelCase		,					num_warmup_steps=_lowerCamelCase		,					last_epoch=_lowerCamelCase					)

 # 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(
      _lowerCamelCase		,					num_warmup_steps=_lowerCamelCase		,					num_training_steps=_lowerCamelCase		,					num_cycles=_lowerCamelCase		,					last_epoch=_lowerCamelCase		,					)

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

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


'''simple docstring'''

def       __UpperCamelCase	(       ):
 lowercase__    :   Any						= [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]

 lowercase__    :   Any						= 6
 lowercase__    :   Optional[Any]						= 1
 lowercase__    :   int						= 1901

 lowercase__    :   List[str]						= 0

 while year < 2001:
  day += 7

  if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
   if day > days_per_month[month - 1] and month != 2:
    month += 1
    lowercase__    :   List[Any]						= day - days_per_month[month - 2]
   elif day > 29 and month == 2:
    month += 1
    lowercase__    :   Any						= day - 29
  else:
   if day > days_per_month[month - 1]:
    month += 1
    lowercase__    :   List[Any]						= day - days_per_month[month - 2]

  if month > 12:
   year += 1
   lowercase__    :   Dict						= 1

  if year < 2001 and day == 1:
   sundays += 1
 return sundays


if __name__ == "__main__":
  print(solution())

