


'''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 :Union[str, Any]             =     logging.get_logger(__name__)
class _lowerCamelCase			(		__UpperCamelCase				):



		'''simple docstring'''







		A_      :     int        =							'linear'
		A_      :     Union[str, Any]        =							'cosine'
		A_      :     Union[str, Any]        =							'cosine_with_restarts'
		A_      :     Union[str, Any]        =							'polynomial'
		A_      :     str        =							'constant'
		A_      :     Union[str, Any]        =							'constant_with_warmup'
		A_      :     Tuple        =							'piecewise_constant'







def lowerCamelCase     (     lowerCAmelCase							:      List[str]						,						lowerCAmelCase							:      List[str] = -1						):





	"""simple docstring"""







	return LambdaLR(__lowerCAmelCase						,						lambda lowerCAmelCase						: 1						,						last_epoch=__lowerCAmelCase						)





def lowerCamelCase     (     lowerCAmelCase							:      Dict						,						lowerCAmelCase							:      List[str]						,						lowerCAmelCase							:      int = -1						):





	"""simple docstring"""







	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 lowerCamelCase     (     lowerCAmelCase							:      Tuple						,						lowerCAmelCase							:      int						,						lowerCAmelCase							:      Union[str, Any] = -1						):





	"""simple docstring"""







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

	def create_rules_function(lowerCAmelCase							:      Union[str, Any]						,						lowerCAmelCase							:      int						):
		def rule_func(lowerCAmelCase							:      Any						) -> float:
			__magic_name__			:       int	      =	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

	__magic_name__			:       int	      =	create_rules_function(__lowerCAmelCase						,						__lowerCAmelCase						)

	return LambdaLR(__lowerCAmelCase						,						__lowerCAmelCase						,						last_epoch=__lowerCAmelCase						)





def lowerCamelCase     (     lowerCAmelCase							:      Tuple						,						lowerCAmelCase							:      int						,						lowerCAmelCase							:      List[Any]						,						lowerCAmelCase							:      Tuple=-1						):





	"""simple docstring"""







	def lr_lambda(lowerCAmelCase							:      Tuple						):
		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 lowerCamelCase     (     lowerCAmelCase							:      Optional[int]						,						lowerCAmelCase							:      Optional[Any]						,						lowerCAmelCase							:      Optional[int]						,						lowerCAmelCase							:      str = 0.5						,						lowerCAmelCase							:      Optional[Any] = -1						):





	"""simple docstring"""







	def lr_lambda(lowerCAmelCase							:      List[str]						):
		if current_step < num_warmup_steps:
			return float(__lowerCAmelCase						) / float(max(1						,						__lowerCAmelCase						)						)
		__magic_name__			:       List[str]	      =	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 lowerCamelCase     (     lowerCAmelCase							:      Union[str, Any]						,						lowerCAmelCase							:      Dict						,						lowerCAmelCase							:      Optional[Any]						,						lowerCAmelCase							:      List[str] = 1						,						lowerCAmelCase							:      Dict = -1						):





	"""simple docstring"""







	def lr_lambda(lowerCAmelCase							:      Optional[Any]						):
		if current_step < num_warmup_steps:
			return float(__lowerCAmelCase						) / float(max(1						,						__lowerCAmelCase						)						)
		__magic_name__			:       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(__lowerCAmelCase						) * progress) % 1.0)						))						)

	return LambdaLR(__lowerCAmelCase						,						__lowerCAmelCase						,						__lowerCAmelCase						)





def lowerCamelCase     (     lowerCAmelCase							:      List[Any]						,						lowerCAmelCase							:      Any						,						lowerCAmelCase							:      Tuple						,						lowerCAmelCase							:      Dict=1e-7						,						lowerCAmelCase							:      Any=1.0						,						lowerCAmelCase							:      List[Any]=-1						):





	"""simple docstring"""







	__magic_name__			:       Dict	      =	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							:      Union[str, Any]						):
		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:
			__magic_name__			:       Dict	      =	lr_init - lr_end
			__magic_name__			:       Dict	      =	num_training_steps - num_warmup_steps
			__magic_name__			:       Optional[int]	      =	1 - (current_step - num_warmup_steps) / decay_steps
			__magic_name__			:       Optional[Any]	      =	lr_range * pct_remaining**power + lr_end
			return decay / lr_init  # as LambdaLR multiplies by lr_init

	return LambdaLR(__lowerCAmelCase						,						__lowerCAmelCase						,						__lowerCAmelCase						)


lowerCAmelCase :Any             =     {
    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     (     lowerCAmelCase							:      int						,						lowerCAmelCase							:      Union[str, Any]						,						lowerCAmelCase							:      Dict = None						,						lowerCAmelCase							:      Optional[Any] = None						,						lowerCAmelCase							:      Optional[int] = None						,						lowerCAmelCase							:      Any = 1						,						lowerCAmelCase							:      Dict = 1.0						,						lowerCAmelCase							:      Optional[int] = -1						,						):





	"""simple docstring"""







	__magic_name__			:       Union[str, Any]	      =	SchedulerType(__lowerCAmelCase						)
	__magic_name__			:       Union[str, Any]	      =	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						)

def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float(moles / volume				) * nfactor				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float((moles * 0.0_821 * temperature) / (volume)				)				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float((moles * 0.0_821 * temperature) / (pressure)				)				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float((pressure * volume) / (0.0_821 * moles)				)				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



