
"""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


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				_UpperCamelCase ):



							'''simple docstring'''





							_snake_case						      =		'linear'
							_snake_case						      =		'cosine'
							_snake_case						      =		'cosine_with_restarts'
							_snake_case						      =		'polynomial'
							_snake_case						      =		'constant'
							_snake_case						      =		'constant_with_warmup'
							_snake_case						      =		'piecewise_constant'

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase = -1						):
						return LambdaLR(lowerCAmelCase_ ,    lambda _lowerCAmelCase						: 1 ,    last_epoch=lowerCAmelCase_						)

def  lowercase     (_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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = -1						):
						__lowerCAmelCase     =  {}
						__lowerCAmelCase     =  step_rules.split(""","""						)
						for rule_str in rule_list[:-1]:
												__lowerCAmelCase     =  rule_str.split(""":"""						)
												__lowerCAmelCase     =  int(lowerCAmelCase_						)
												__lowerCAmelCase     =  float(lowerCAmelCase_						)
												__lowerCAmelCase     =  value
						__lowerCAmelCase     =  float(rule_list[-1]						)

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

						__lowerCAmelCase     =  create_rules_function(lowerCAmelCase_ ,    lowerCAmelCase_						)

						return LambdaLR(lowerCAmelCase_ ,    lowerCAmelCase_ ,    last_epoch=lowerCAmelCase_						)

def  lowercase     (_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  lowercase     (_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_						)						)
												__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(lowerCAmelCase_						) * 2.0 * progress						))						)

						return LambdaLR(lowerCAmelCase_ ,    lowerCAmelCase_ ,    lowerCAmelCase_						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = 1 ,    _lowerCAmelCase = -1						):

						def lr_lambda(_lowerCAmelCase						):
												if current_step < num_warmup_steps:
																		return float(lowerCAmelCase_						) / float(max(1 ,    lowerCAmelCase_						)						)
												__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(lowerCAmelCase_						) * progress) % 1.0)						))						)

						return LambdaLR(lowerCAmelCase_ ,    lowerCAmelCase_ ,    lowerCAmelCase_						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=1E-7 ,    _lowerCAmelCase=1.0 ,    _lowerCAmelCase=-1						):
						__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(_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:
																		__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(lowerCAmelCase_ ,    lowerCAmelCase_ ,    lowerCAmelCase_						)


SCREAMING_SNAKE_CASE_					     =	{
    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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = None ,    _lowerCAmelCase = None ,    _lowerCAmelCase = None ,    _lowerCAmelCase = 1 ,    _lowerCAmelCase = 1.0 ,    _lowerCAmelCase = -1 ,    ):
						__lowerCAmelCase     =  SchedulerType(lowerCAmelCase_						)
						__lowerCAmelCase     =  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 .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




