

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__  :      List[str]			=    logging.get_logger(__name__)



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	str						 =  'linear'
  __lowerCAmelCase     :	Any						 =  'cosine'
  __lowerCAmelCase     :	List[Any]						 =  'cosine_with_restarts'
  __lowerCAmelCase     :	int						 =  'polynomial'
  __lowerCAmelCase     :	int						 =  'constant'
  __lowerCAmelCase     :	Optional[Any]						 =  'constant_with_warmup'
  __lowerCAmelCase     :	Tuple						 =  'piecewise_constant'






def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optimizer ,   _SCREAMING_SNAKE_CASE					:		int = -1       ):





    """simple docstring"""


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


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optimizer ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int = -1       ):





    """simple docstring"""


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

    return LambdaLR(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   last_epoch=_SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optimizer ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		int = -1       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{}
    SCREAMING_SNAKE_CASE_									=						step_rules.split(','       )
    for rule_str in rule_list[:-1]:
        SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						rule_str.split(':'       )
        SCREAMING_SNAKE_CASE_									=						int(_SCREAMING_SNAKE_CASE       )
        SCREAMING_SNAKE_CASE_									=						float(_SCREAMING_SNAKE_CASE       )
        SCREAMING_SNAKE_CASE_									=						value
    SCREAMING_SNAKE_CASE_									=						float(rule_list[-1]       )

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

        return rule_func

    SCREAMING_SNAKE_CASE_									=						create_rules_function(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

    return LambdaLR(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   last_epoch=_SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		Any=-1       ):





    """simple docstring"""


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

    return LambdaLR(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optimizer ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		float = 0.5 ,   _SCREAMING_SNAKE_CASE					:		int = -1       ):





    """simple docstring"""


    def lr_lambda(_SCREAMING_SNAKE_CASE					:		Dict       ):
        if current_step < num_warmup_steps:
            return float(_SCREAMING_SNAKE_CASE       ) / float(max(1 ,   _SCREAMING_SNAKE_CASE       )       )
        SCREAMING_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(_SCREAMING_SNAKE_CASE       ) * 2.0 * progress       ))       )

    return LambdaLR(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optimizer ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int = 1 ,   _SCREAMING_SNAKE_CASE					:		int = -1       ):





    """simple docstring"""


    def lr_lambda(_SCREAMING_SNAKE_CASE					:		Tuple       ):
        if current_step < num_warmup_steps:
            return float(_SCREAMING_SNAKE_CASE       ) / float(max(1 ,   _SCREAMING_SNAKE_CASE       )       )
        SCREAMING_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(_SCREAMING_SNAKE_CASE       ) * progress) % 1.0)       ))       )

    return LambdaLR(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		List[str] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any]=1E-7 ,   _SCREAMING_SNAKE_CASE					:		List[str]=1.0 ,   _SCREAMING_SNAKE_CASE					:		int=-1       ):





    """simple docstring"""


    SCREAMING_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(_SCREAMING_SNAKE_CASE					:		int       ):
        if current_step < num_warmup_steps:
            return float(_SCREAMING_SNAKE_CASE       ) / float(max(1 ,   _SCREAMING_SNAKE_CASE       )       )
        elif current_step > num_training_steps:
            return lr_end / lr_init  # as LambdaLR multiplies by lr_init
        else:
            SCREAMING_SNAKE_CASE_									=						lr_init - lr_end
            SCREAMING_SNAKE_CASE_									=						num_training_steps - num_warmup_steps
            SCREAMING_SNAKE_CASE_									=						1 - (current_step - num_warmup_steps) / decay_steps
            SCREAMING_SNAKE_CASE_									=						lr_range * pct_remaining**power + lr_end
            return decay / lr_init  # as LambdaLR multiplies by lr_init

    return LambdaLR(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )


UpperCamelCase__  :      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       (  _SCREAMING_SNAKE_CASE					:		Union[str, SchedulerType] ,   _SCREAMING_SNAKE_CASE					:		Optimizer ,   _SCREAMING_SNAKE_CASE					:		Optional[str] = None ,   _SCREAMING_SNAKE_CASE					:		Optional[int] = None ,   _SCREAMING_SNAKE_CASE					:		Optional[int] = None ,   _SCREAMING_SNAKE_CASE					:		int = 1 ,   _SCREAMING_SNAKE_CASE					:		float = 1.0 ,   _SCREAMING_SNAKE_CASE					:		int = -1 ,   ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						SchedulerType(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						TYPE_TO_SCHEDULER_FUNCTION[name]
    if name == SchedulerType.CONSTANT:
        return schedule_func(_SCREAMING_SNAKE_CASE ,   last_epoch=_SCREAMING_SNAKE_CASE       )

    if name == SchedulerType.PIECEWISE_CONSTANT:
        return schedule_func(_SCREAMING_SNAKE_CASE ,   step_rules=_SCREAMING_SNAKE_CASE ,   last_epoch=_SCREAMING_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(_SCREAMING_SNAKE_CASE ,   num_warmup_steps=_SCREAMING_SNAKE_CASE ,   last_epoch=_SCREAMING_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(
            _SCREAMING_SNAKE_CASE ,   num_warmup_steps=_SCREAMING_SNAKE_CASE ,   num_training_steps=_SCREAMING_SNAKE_CASE ,   num_cycles=_SCREAMING_SNAKE_CASE ,   last_epoch=_SCREAMING_SNAKE_CASE ,   )

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

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








def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		list ,   _SCREAMING_SNAKE_CASE					:		list ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    if index == number_of_items:
        return 0
    SCREAMING_SNAKE_CASE_									=						0
    SCREAMING_SNAKE_CASE_									=						0
    SCREAMING_SNAKE_CASE_									=						knapsack(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   index + 1       )
    if weights[index] <= max_weight:
        SCREAMING_SNAKE_CASE_									=						values[index] + knapsack(
            _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   max_weight - weights[index] ,   index + 1       )
    return max(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


