





"""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


_UpperCamelCase					      =   logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							Optional[Any]									=		"""linear"""
				__snake_case :							Union[str, Any]									=		"""cosine"""
				__snake_case :							Any									=		"""cosine_with_restarts"""
				__snake_case :							Union[str, Any]									=		"""polynomial"""
				__snake_case :							Tuple									=		"""constant"""
				__snake_case :							Dict									=		"""constant_with_warmup"""
				__snake_case :							int									=		"""piecewise_constant"""



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Optimizer     ,    SCREAMING_SNAKE_CASE							:				int = -1 ):


							'''simple docstring'''







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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Optimizer     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				int = -1 ):


							'''simple docstring'''







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

							return LambdaLR(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    last_epoch=SCREAMING_SNAKE_CASE )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Optimizer     ,    SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int = -1 ):


							'''simple docstring'''







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

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

														return rule_func

							__lowerCamelCase   :   Optional[int]       =create_rules_function(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

							return LambdaLR(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    last_epoch=SCREAMING_SNAKE_CASE )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Dict     ,    SCREAMING_SNAKE_CASE							:				Optional[int]     ,    SCREAMING_SNAKE_CASE							:				List[Any]     ,    SCREAMING_SNAKE_CASE							:				Dict=-1 ):


							'''simple docstring'''







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

							return LambdaLR(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Optimizer     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float = 0.5     ,    SCREAMING_SNAKE_CASE							:				int = -1 ):


							'''simple docstring'''







							def lr_lambda(SCREAMING_SNAKE_CASE							:				int ):
														if current_step < num_warmup_steps:
																					return float(SCREAMING_SNAKE_CASE ) / float(max(1     ,    SCREAMING_SNAKE_CASE ) )
														__lowerCamelCase   :   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(SCREAMING_SNAKE_CASE ) * 2.0 * progress )) )

							return LambdaLR(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Optimizer     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				int = 1     ,    SCREAMING_SNAKE_CASE							:				int = -1 ):


							'''simple docstring'''







							def lr_lambda(SCREAMING_SNAKE_CASE							:				Tuple ):
														if current_step < num_warmup_steps:
																					return float(SCREAMING_SNAKE_CASE ) / float(max(1     ,    SCREAMING_SNAKE_CASE ) )
														__lowerCamelCase   :   Optional[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(SCREAMING_SNAKE_CASE ) * progress) % 1.0) )) )

							return LambdaLR(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				List[str]     ,    SCREAMING_SNAKE_CASE							:				Any=1E-7     ,    SCREAMING_SNAKE_CASE							:				List[Any]=1.0     ,    SCREAMING_SNAKE_CASE							:				Tuple=-1 ):


							'''simple docstring'''







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

							return LambdaLR(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )


_UpperCamelCase					      =   {
    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_      (   SCREAMING_SNAKE_CASE							:				Union[str, SchedulerType]     ,    SCREAMING_SNAKE_CASE							:				Optimizer     ,    SCREAMING_SNAKE_CASE							:				Optional[str] = None     ,    SCREAMING_SNAKE_CASE							:				Optional[int] = None     ,    SCREAMING_SNAKE_CASE							:				Optional[int] = None     ,    SCREAMING_SNAKE_CASE							:				int = 1     ,    SCREAMING_SNAKE_CASE							:				float = 1.0     ,    SCREAMING_SNAKE_CASE							:				int = -1     ,    ):


							'''simple docstring'''







							__lowerCamelCase   :   Union[str, Any]       =SchedulerType(SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   Tuple       =TYPE_TO_SCHEDULER_FUNCTION[name]
							if name == SchedulerType.CONSTANT:
														return schedule_func(SCREAMING_SNAKE_CASE     ,    last_epoch=SCREAMING_SNAKE_CASE )

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

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

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











"""simple docstring"""





from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
							import accelerate



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							if not is_accelerate_available():
														return method
							__lowerCamelCase   :   Dict       =version.parse(accelerate.__version__ ).base_version
							if version.parse(SCREAMING_SNAKE_CASE ) < version.parse('''0.17.0''' ):
														return method

							def wrapper(self							:				List[Any]     ,    *SCREAMING_SNAKE_CASE							:				Any     ,    **SCREAMING_SNAKE_CASE							:				List[str] ):
														if hasattr(self     ,    '''_hf_hook''' ) and hasattr(self._hf_hook     ,    '''pre_forward''' ):
																					self._hf_hook.pre_forward(self )
														return method(self     ,    *SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE )

							return wrapper




