





'''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


__lowerCAmelCase							:	Any			=logging.get_logger(__name__)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""linear"""
 __lowercase   		=				"""cosine"""
 __lowercase   		=				"""cosine_with_restarts"""
 __lowercase   		=				"""polynomial"""
 __lowercase   		=				"""constant"""
 __lowercase   		=				"""constant_with_warmup"""
 __lowercase   		=				"""piecewise_constant"""





def 						UpperCamelCase				(							_lowerCamelCase					:					Optimizer						,       _lowerCamelCase					:					int = -1       ):
    return LambdaLR(_lowerCamelCase						,       lambda _lowerCamelCase       : 1						,       last_epoch=_lowerCamelCase       )





def 						UpperCamelCase				(							_lowerCamelCase					:					Optimizer						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					int = -1       ):

    def lr_lambda(_lowerCamelCase					:					int       ):
        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				(							_lowerCamelCase					:					Optimizer						,       _lowerCamelCase					:					str						,       _lowerCamelCase					:					int = -1       ):
    A__				=			{}
    A__				=			step_rules.split(","       )
    for rule_str in rule_list[:-1]:
        A__,	A__				=			rule_str.split(":"       )
        A__				=			int(_lowerCamelCase       )
        A__				=			float(_lowerCamelCase       )
        A__				=			value
    A__				=			float(rule_list[-1]       )

    def create_rules_function(_lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					List[str]       ):
        def rule_func(_lowerCamelCase					:					int       ) -> float:
            A__				=			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

    A__				=			create_rules_function(_lowerCamelCase						,       _lowerCamelCase       )

    return LambdaLR(_lowerCamelCase						,       _lowerCamelCase						,       last_epoch=_lowerCamelCase       )





def 						UpperCamelCase				(							_lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Any						,       _lowerCamelCase					:					List[str]=-1       ):

    def lr_lambda(_lowerCamelCase					:					int       ):
        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				(							_lowerCamelCase					:					Optimizer						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					float = 0.5						,       _lowerCamelCase					:					int = -1       ):

    def lr_lambda(_lowerCamelCase					:					Union[str, Any]       ):
        if current_step < num_warmup_steps:
            return float(_lowerCamelCase       ) / float(max(1						,       _lowerCamelCase       )       )
        A__				=			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				(							_lowerCamelCase					:					Optimizer						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					int = 1						,       _lowerCamelCase					:					int = -1       ):

    def lr_lambda(_lowerCamelCase					:					Optional[int]       ):
        if current_step < num_warmup_steps:
            return float(_lowerCamelCase       ) / float(max(1						,       _lowerCamelCase       )       )
        A__				=			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				(							_lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					Dict						,       _lowerCamelCase					:					Dict						,       _lowerCamelCase					:					Any=1e-7						,       _lowerCamelCase					:					Any=1.0						,       _lowerCamelCase					:					Any=-1       ):
    A__				=			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					:					int       ):
        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:
            A__				=			lr_init - lr_end
            A__				=			num_training_steps - num_warmup_steps
            A__				=			1 - (current_step - num_warmup_steps) / decay_steps
            A__				=			lr_range * pct_remaining**power + lr_end
            return decay / lr_init  # as LambdaLR multiplies by lr_init

    return LambdaLR(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )


__lowerCAmelCase							:	int			={
    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				(							_lowerCamelCase					:					Union[str, SchedulerType]						,       _lowerCamelCase					:					Optimizer						,       _lowerCamelCase					:					Optional[str] = None						,       _lowerCamelCase					:					Optional[int] = None						,       _lowerCamelCase					:					Optional[int] = None						,       _lowerCamelCase					:					int = 1						,       _lowerCamelCase					:					float = 1.0						,       _lowerCamelCase					:					int = -1						,       ):
    A__				=			SchedulerType(_lowerCamelCase       )
    A__				=			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'''

from ..utils import DummyObject, requires_backends






class    UpperCAmelCase	(       metaclass=UpperCamelCase__				):
 __lowercase   		=				["""note_seq"""]
 def __init__(				self      :Optional[Any]						,       *lowercase_      :List[Any]						,       **lowercase_      :List[str]							)->							int:
     requires_backends(self						,       ["note_seq"]							)
 @classmethod
 def      UpperCAmelCase_							(				cls      :str						,       *lowercase_      :Union[str, Any]						,       **lowercase_      :Any							)->							Optional[int]:
     requires_backends(cls						,       ["note_seq"]							)



 @classmethod
 def      UpperCAmelCase_							(				cls      :Dict						,       *lowercase_      :Tuple						,       **lowercase_      :List[Any]							)->							Optional[Any]:
     requires_backends(cls						,       ["note_seq"]							)



