
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__  							=	logging.get_logger(__name__)




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		int     						=  'linear'
      _A    :		Union[str, Any]     						=  'cosine'
      _A    :		Union[str, Any]     						=  'cosine_with_restarts'
      _A    :		Union[str, Any]     						=  'polynomial'
      _A    :		str     						=  'constant'
      _A    :		Union[str, Any]     						=  'constant_with_warmup'
      _A    :		Tuple     						=  'piecewise_constant'





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase = -1				):
    return LambdaLR(__lowerCAmelCase     ,		lambda __lowerCAmelCase				: 1     ,		last_epoch=__lowerCAmelCase				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase = -1				):

    def lr_lambda(__lowerCAmelCase				):
        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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase = -1				):
    snake_case__    =					{}
    snake_case__    =					step_rules.split(","				)
    for rule_str in rule_list[:-1]:
        snake_case__    ,       snake_case__    =					rule_str.split(":"				)
        snake_case__    =					int(__lowerCAmelCase				)
        snake_case__    =					float(__lowerCAmelCase				)
        snake_case__    =					value
    snake_case__    =					float(rule_list[-1]				)

    def create_rules_function(__lowerCAmelCase     ,		__lowerCAmelCase				):
        def rule_func(__lowerCAmelCase				) -> float:
            snake_case__    =					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

    snake_case__    =					create_rules_function(__lowerCAmelCase     ,		__lowerCAmelCase				)

    return LambdaLR(__lowerCAmelCase     ,		__lowerCAmelCase     ,		last_epoch=__lowerCAmelCase				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase=-1				):

    def lr_lambda(__lowerCAmelCase				):
        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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase = 0.5     ,		__lowerCAmelCase = -1				):

    def lr_lambda(__lowerCAmelCase				):
        if current_step < num_warmup_steps:
            return float(__lowerCAmelCase				) / float(max(1     ,		__lowerCAmelCase				)				)
        snake_case__    =					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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase = 1     ,		__lowerCAmelCase = -1				):

    def lr_lambda(__lowerCAmelCase				):
        if current_step < num_warmup_steps:
            return float(__lowerCAmelCase				) / float(max(1     ,		__lowerCAmelCase				)				)
        snake_case__    =					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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase=1E-7     ,		__lowerCAmelCase=1.0     ,		__lowerCAmelCase=-1				):
    snake_case__    =					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(__lowerCAmelCase				):
        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:
            snake_case__    =					lr_init - lr_end
            snake_case__    =					num_training_steps - num_warmup_steps
            snake_case__    =					1 - (current_step - num_warmup_steps) / decay_steps
            snake_case__    =					lr_range * pct_remaining**power + lr_end
            return decay / lr_init  # as LambdaLR multiplies by lr_init

    return LambdaLR(__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				)


__magic_name__  							=	{
    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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase = None     ,		__lowerCAmelCase = None     ,		__lowerCAmelCase = None     ,		__lowerCAmelCase = 1     ,		__lowerCAmelCase = 1.0     ,		__lowerCAmelCase = -1     ,		):
    snake_case__    =					SchedulerType(__lowerCAmelCase				)
    snake_case__    =					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				)





from math import loga





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if a < 0:
        raise ValueError("Input value must be a positive integer"				)
    elif isinstance(__lowerCAmelCase     ,		__lowerCAmelCase				):
        raise TypeError("Input value must be a 'int' type"				)
    return 0 if (a == 0) else int(loga(a & -a				)				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



