






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


__magic_name__:					Any							=     logging.get_logger(__name__)





class 	snake_case__ (							SCREAMING_SNAKE_CASE_				):
	lowercase__						:					Optional[int]											=   '''linear'''
	lowercase__						:					Union[str, Any]											=   '''cosine'''
	lowercase__						:					Tuple											=   '''cosine_with_restarts'''
	lowercase__						:					Optional[int]											=   '''polynomial'''
	lowercase__						:					int											=   '''constant'''
	lowercase__						:					int											=   '''constant_with_warmup'''
	lowercase__						:					Optional[Any]											=   '''piecewise_constant'''



def    UpperCamelCase						(	_A,   _A = -1			):






						"""simple docstring"""







						return LambdaLR(_A,   lambda _A			: 1,   last_epoch=_A			)

def    UpperCamelCase						(	_A,   _A,   _A = -1			):






						"""simple docstring"""







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

						return LambdaLR(_A,   _A,   last_epoch=_A			)

def    UpperCamelCase						(	_A,   _A,   _A = -1			):






						"""simple docstring"""







						__magic_name__      :       Tuple       						= {}
						__magic_name__      :       List[Any]       						= step_rules.split(""","""			)
						for rule_str in rule_list[:-1]:
												__magic_name__      :       int       						= rule_str.split(""":"""			)
												__magic_name__      :       int       						= int(_A			)
												__magic_name__      :       str       						= float(_A			)
												__magic_name__      :       Optional[Any]       						= value
						__magic_name__      :       List[Any]       						= float(rule_list[-1]			)

						def create_rules_function(_A,   _A			):
												def rule_func(_A			) -> float:
																		__magic_name__      :       List[Any]       						= sorted(rules_dict.keys()			)
																		for i, sorted_step in enumerate(_A			):
																								if steps < sorted_step:
																														return rules_dict[sorted_steps[i]]
																		return last_lr_multiple

												return rule_func

						__magic_name__      :       Dict       						= create_rules_function(_A,   _A			)

						return LambdaLR(_A,   _A,   last_epoch=_A			)

def    UpperCamelCase						(	_A,   _A,   _A,   _A=-1			):






						"""simple docstring"""







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

						return LambdaLR(_A,   _A,   _A			)

def    UpperCamelCase						(	_A,   _A,   _A,   _A = 0.5,   _A = -1			):






						"""simple docstring"""







						def lr_lambda(_A			):
												if current_step < num_warmup_steps:
																		return float(_A			) / float(max(1,   _A			)			)
												__magic_name__      :       Optional[int]       						= 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(_A			) * 2.0 * progress			))			)

						return LambdaLR(_A,   _A,   _A			)

def    UpperCamelCase						(	_A,   _A,   _A,   _A = 1,   _A = -1			):






						"""simple docstring"""







						def lr_lambda(_A			):
												if current_step < num_warmup_steps:
																		return float(_A			) / float(max(1,   _A			)			)
												__magic_name__      :       int       						= 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(_A			) * progress) % 1.0)			))			)

						return LambdaLR(_A,   _A,   _A			)

def    UpperCamelCase						(	_A,   _A,   _A,   _A=1e-7,   _A=1.0,   _A=-1			):






						"""simple docstring"""







						__magic_name__      :       Optional[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(_A			):
												if current_step < num_warmup_steps:
																		return float(_A			) / float(max(1,   _A			)			)
												elif current_step > num_training_steps:
																		return lr_end / lr_init  # as LambdaLR multiplies by lr_init
												else:
																		__magic_name__      :       List[str]       						= lr_init - lr_end
																		__magic_name__      :       Optional[int]       						= num_training_steps - num_warmup_steps
																		__magic_name__      :       Optional[Any]       						= 1 - (current_step - num_warmup_steps) / decay_steps
																		__magic_name__      :       Optional[int]       						= lr_range * pct_remaining**power + lr_end
																		return decay / lr_init  # as LambdaLR multiplies by lr_init

						return LambdaLR(_A,   _A,   _A			)


__magic_name__:					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						(	_A,   _A,   _A = None,   _A = None,   _A = None,   _A = 1,   _A = 1.0,   _A = -1,   ):






						"""simple docstring"""







						__magic_name__      :       str       						= SchedulerType(_A			)
						__magic_name__      :       Optional[Any]       						= TYPE_TO_SCHEDULER_FUNCTION[name]
						if name == SchedulerType.CONSTANT:
												return schedule_func(_A,   last_epoch=_A			)

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

						# 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(_A,   num_warmup_steps=_A,   last_epoch=_A			)

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

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

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














def    UpperCamelCase						(	_A = 1,   _A = 1000			):






						"""simple docstring"""







						__magic_name__      :       Optional[int]       						= 1
						__magic_name__      :       Dict       						= 0

						for divide_by_number in range(_A,   digit + 1			):
												__magic_name__      :       list[int]       						= []
												__magic_name__      :       Any       						= numerator
												for _ in range(1,   digit + 1			):
																		if now_divide in has_been_divided:
																								if longest_list_length < len(_A			):
																														__magic_name__      :       int       						= len(_A			)
																														__magic_name__      :       Dict       						= divide_by_number
																		else:
																								has_been_divided.append(_A			)
																								__magic_name__      :       Optional[int]       						= now_divide * 10 % divide_by_number

						return the_digit


# Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()
