'''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


__UpperCAmelCase												=logging.get_logger(__name__)




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					str							="linear"
     lowerCamelCase  :					int							="cosine"
     lowerCamelCase  :					Union[str, Any]							="cosine_with_restarts"
     lowerCamelCase  :					Tuple							="polynomial"
     lowerCamelCase  :					Dict							="constant"
     lowerCamelCase  :					Dict							="constant_with_warmup"
     lowerCamelCase  :					Union[str, Any]							="piecewise_constant"






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__ = -1    )     ->   Dict:
    return LambdaLR(UpperCamelCase__    ,  lambda UpperCamelCase__    : 1    ,  last_epoch=UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__ = -1    )     ->   Any:

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

    return LambdaLR(UpperCamelCase__    ,  UpperCamelCase__    ,  last_epoch=UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__ = -1    )     ->   Any:
    __lowerCamelCase          =  {}
    __lowerCamelCase          =  step_rules.split(''','''    )
    for rule_str in rule_list[:-1]:
        __lowerCamelCase     ,  __lowerCamelCase          =  rule_str.split(''':'''    )
        __lowerCamelCase          =  int(UpperCamelCase__    )
        __lowerCamelCase          =  float(UpperCamelCase__    )
        __lowerCamelCase          =  value
    __lowerCamelCase          =  float(rule_list[-1]    )

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

        return rule_func

    __lowerCamelCase          =  create_rules_function(UpperCamelCase__    ,  UpperCamelCase__    )

    return LambdaLR(UpperCamelCase__    ,  UpperCamelCase__    ,  last_epoch=UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=-1    )     ->   Optional[int]:

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

    return LambdaLR(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__ = 0.5    ,  UpperCamelCase__ = -1    )     ->   int:

    def lr_lambda(UpperCamelCase__    ):
        if current_step < num_warmup_steps:
            return float(UpperCamelCase__    ) / float(max(1    ,  UpperCamelCase__    )    )
        __lowerCamelCase          =  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(UpperCamelCase__    ) * 2.0 * progress    ))    )

    return LambdaLR(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__ = 1    ,  UpperCamelCase__ = -1    )     ->   Any:

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

    return LambdaLR(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=1E-7    ,  UpperCamelCase__=1.0    ,  UpperCamelCase__=-1    )     ->   Dict:
    __lowerCamelCase          =  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(UpperCamelCase__    ) / float(max(1    ,  UpperCamelCase__    )    )
        elif current_step > num_training_steps:
            return lr_end / lr_init  # as LambdaLR multiplies by lr_init
        else:
            __lowerCamelCase          =  lr_init - lr_end
            __lowerCamelCase          =  num_training_steps - num_warmup_steps
            __lowerCamelCase          =  1 - (current_step - num_warmup_steps) / decay_steps
            __lowerCamelCase          =  lr_range * pct_remaining**power + lr_end
            return decay / lr_init  # as LambdaLR multiplies by lr_init

    return LambdaLR(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )


__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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__ = None    ,  UpperCamelCase__ = None    ,  UpperCamelCase__ = None    ,  UpperCamelCase__ = 1    ,  UpperCamelCase__ = 1.0    ,  UpperCamelCase__ = -1    ,  )     ->   int:
    __lowerCamelCase          =  SchedulerType(UpperCamelCase__    )
    __lowerCamelCase          =  TYPE_TO_SCHEDULER_FUNCTION[name]
    if name == SchedulerType.CONSTANT:
        return schedule_func(UpperCamelCase__    ,  last_epoch=UpperCamelCase__    )

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

    # 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(UpperCamelCase__    ,  num_warmup_steps=UpperCamelCase__    ,  last_epoch=UpperCamelCase__    )

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

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

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

'''simple docstring'''




def __lowerCAmelCase			(							UpperCamelCase__    )     ->   str:
    return "".join(chr(ord(UpperCamelCase__    ) - 32    ) if '''a''' <= char <= '''z''' else char for char in word    )


if __name__ == "__main__":
  from doctest import testmod

  testmod()
