


"""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


a__				:							int						=     logging.get_logger(__name__)




class  UpperCamelCase_	( __a):

	"""simple docstring"""





	snake_case__				:  Tuple    					=			"linear"
	snake_case__				:  List[str]    					=			"cosine"
	snake_case__				:  Optional[int]    					=			"cosine_with_restarts"
	snake_case__				:  Optional[int]    					=			"polynomial"
	snake_case__				:  List[str]    					=			"constant"
	snake_case__				:  List[str]    					=			"constant_with_warmup"
	snake_case__				:  str    					=			"piecewise_constant"




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_ = -1   ):




					'''simple docstring'''


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




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




					'''simple docstring'''


					def lr_lambda(lowerCAmelCase_   ):
										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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_ = -1   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				{}
					__SCREAMING_SNAKE_CASE						   =				step_rules.split(","   )
					for rule_str in rule_list[:-1]:
										__SCREAMING_SNAKE_CASE						   =				rule_str.split(":"   )
										__SCREAMING_SNAKE_CASE						   =				int(snake_case__   )
										__SCREAMING_SNAKE_CASE						   =				float(snake_case__   )
										__SCREAMING_SNAKE_CASE						   =				value
					__SCREAMING_SNAKE_CASE						   =				float(rule_list[-1]   )

					def create_rules_function(lowerCAmelCase_  ,						lowerCAmelCase_   ):
										def rule_func(lowerCAmelCase_   ) -> float:
															__SCREAMING_SNAKE_CASE						   =				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

					__SCREAMING_SNAKE_CASE						   =				create_rules_function(snake_case__  ,						snake_case__   )

					return LambdaLR(snake_case__  ,						snake_case__  ,						last_epoch=snake_case__   )




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




					'''simple docstring'''


					def lr_lambda(lowerCAmelCase_   ):
										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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_ = 0.5  ,						lowerCAmelCase_ = -1   ):




					'''simple docstring'''


					def lr_lambda(lowerCAmelCase_   ):
										if current_step < num_warmup_steps:
															return float(snake_case__   ) / float(max(1  ,						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(snake_case__   ) * 2.0 * progress   ))   )

					return LambdaLR(snake_case__  ,						snake_case__  ,						snake_case__   )




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




					'''simple docstring'''


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

					return LambdaLR(snake_case__  ,						snake_case__  ,						snake_case__   )




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


a__				:							Optional[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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = 1  ,						lowerCAmelCase_ = 1.0  ,						lowerCAmelCase_ = -1  ,						):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				SchedulerType(snake_case__   )
					__SCREAMING_SNAKE_CASE						   =				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"""


from ..utils import DummyObject, requires_backends






class 					lowercase(     metaclass=__a ):







 '''simple docstring'''





 lowercase__ 					=					["note_seq"]


 def __init__(				self:		Dict,      *a_:		Union[str, Any],      **a_:		List[str]		):




   '''simple docstring'''



   requires_backends(self,      ["""note_seq"""]		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Optional[int],      *a_:		Any,      **a_:		Optional[Any]		):




   '''simple docstring'''



   requires_backends(cls,      ["""note_seq"""]		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Tuple,      *a_:		Optional[Any],      **a_:		List[str]		):




   '''simple docstring'''



   requires_backends(cls,      ["""note_seq"""]		)


