
'''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


lowercase			:				Any    		=			logging.get_logger(__name__)





class  A				(							__snake_case						):
     __magic_name__						=		'''linear'''
     __magic_name__						=		'''cosine'''
     __magic_name__						=		'''cosine_with_restarts'''
     __magic_name__						=		'''polynomial'''
     __magic_name__						=		'''constant'''
     __magic_name__						=		'''constant_with_warmup'''
     __magic_name__						=		'''piecewise_constant'''







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__ = -1						):




    '''simple docstring'''



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







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__ = -1						):




    '''simple docstring'''



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

    return LambdaLR(snake_case__		,				snake_case__		,				last_epoch=snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__ = -1						):




    '''simple docstring'''



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

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

        return rule_func

    A       :   Any					=						create_rules_function(snake_case__		,				snake_case__						)

    return LambdaLR(snake_case__		,				snake_case__		,				last_epoch=snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=-1						):




    '''simple docstring'''



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

    return LambdaLR(snake_case__		,				snake_case__		,				snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__ = 0.5		,				snake_case__ = -1						):




    '''simple docstring'''



    def lr_lambda(snake_case__						):
        if current_step < num_warmup_steps:
            return float(snake_case__						) / float(max(1		,				snake_case__						)						)
        A       :   Union[str, Any]					=						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(snake_case__						) * 2.0 * progress						))						)

    return LambdaLR(snake_case__		,				snake_case__		,				snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__ = 1		,				snake_case__ = -1						):




    '''simple docstring'''



    def lr_lambda(snake_case__						):
        if current_step < num_warmup_steps:
            return float(snake_case__						) / float(max(1		,				snake_case__						)						)
        A       :   List[Any]					=						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(snake_case__						) * progress) % 1.0)						))						)

    return LambdaLR(snake_case__		,				snake_case__		,				snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=1E-7		,				snake_case__=1.0		,				snake_case__=-1						):




    '''simple docstring'''



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

    return LambdaLR(snake_case__		,				snake_case__		,				snake_case__						)


lowercase			:				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 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__ = None		,				snake_case__ = None		,				snake_case__ = None		,				snake_case__ = 1		,				snake_case__ = 1.0		,				snake_case__ = -1		,				):




    '''simple docstring'''



    A       :   str					=						SchedulerType(snake_case__						)
    A       :   Dict					=						TYPE_TO_SCHEDULER_FUNCTION[name]
    if name == SchedulerType.CONSTANT:
        return schedule_func(snake_case__		,				last_epoch=snake_case__						)

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

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

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






'''simple docstring'''




def 		lowerCAmelCase_				(   snake_case__ = 10						):




    '''simple docstring'''



    if not isinstance(snake_case__		,				snake_case__						) or n < 0:
        raise ValueError('''Invalid input'''						)
    A       :   List[str]					=						10**n
    A       :   Tuple					=						2_8433 * (pow(2		,				783_0457		,				snake_case__						)) + 1
    return str(number % modulus						)


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

      testmod()
      print(f'''{solution(10) = }''')




