



'''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__ :			Dict 						=				logging.get_logger(__name__)






class 		_UpperCamelCase						(							a__					):


			'''simple docstring'''

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


def        lowerCAmelCase_     (			_lowerCamelCase:     List[Any] ,   _lowerCamelCase:     List[Any] = -1      ):
		return LambdaLR(_lowerCamelCase ,   lambda _lowerCamelCase      : 1 ,   last_epoch=_lowerCamelCase      )


def        lowerCAmelCase_     (			_lowerCamelCase:     str ,   _lowerCamelCase:     List[str] ,   _lowerCamelCase:     List[str] = -1      ):

		def lr_lambda(_lowerCamelCase:     Optional[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:     List[Any] ,   _lowerCamelCase:     Dict ,   _lowerCamelCase:     int = -1      ):
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{}
		__SCREAMING_SNAKE_CASE :	int					 =	step_rules.split(""","""      )
		for rule_str in rule_list[:-1]:
				__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	List[str]					 =	rule_str.split(""":"""      )
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	int(_lowerCamelCase      )
				__SCREAMING_SNAKE_CASE :	Dict					 =	float(_lowerCamelCase      )
				__SCREAMING_SNAKE_CASE :	int					 =	value
		__SCREAMING_SNAKE_CASE :	Dict					 =	float(rule_list[-1]      )

		def create_rules_function(_lowerCamelCase:     str ,   _lowerCamelCase:     Optional[int]      ):
				def rule_func(_lowerCamelCase:     List[Any]      ) -> float:
						__SCREAMING_SNAKE_CASE :	Optional[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

		__SCREAMING_SNAKE_CASE :	List[str]					 =	create_rules_function(_lowerCamelCase ,   _lowerCamelCase      )

		return LambdaLR(_lowerCamelCase ,   _lowerCamelCase ,   last_epoch=_lowerCamelCase      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Tuple ,   _lowerCamelCase:     Optional[Any] ,   _lowerCamelCase:     Tuple ,   _lowerCamelCase:     Optional[Any]=-1      ):

		def lr_lambda(_lowerCamelCase:     Optional[int]      ):
				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:     Any ,   _lowerCamelCase:     str ,   _lowerCamelCase:     int ,   _lowerCamelCase:     Dict = 0.5 ,   _lowerCamelCase:     int = -1      ):

		def lr_lambda(_lowerCamelCase:     str      ):
				if current_step < num_warmup_steps:
						return float(_lowerCamelCase      ) / float(max(1 ,   _lowerCamelCase      )      )
				__SCREAMING_SNAKE_CASE :	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:     Tuple ,   _lowerCamelCase:     Tuple ,   _lowerCamelCase:     List[Any] ,   _lowerCamelCase:     Dict = 1 ,   _lowerCamelCase:     Union[str, Any] = -1      ):

		def lr_lambda(_lowerCamelCase:     Tuple      ):
				if current_step < num_warmup_steps:
						return float(_lowerCamelCase      ) / float(max(1 ,   _lowerCamelCase      )      )
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	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:     int ,   _lowerCamelCase:     Any ,   _lowerCamelCase:     Optional[Any] ,   _lowerCamelCase:     List[Any]=1E-7 ,   _lowerCamelCase:     str=1.0 ,   _lowerCamelCase:     Union[str, Any]=-1      ):
		__SCREAMING_SNAKE_CASE :	Tuple					 =	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:     Tuple      ):
				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:
						__SCREAMING_SNAKE_CASE :	List[str]					 =	lr_init - lr_end
						__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	num_training_steps - num_warmup_steps
						__SCREAMING_SNAKE_CASE :	int					 =	1 - (current_step - num_warmup_steps) / decay_steps
						__SCREAMING_SNAKE_CASE :	Tuple					 =	lr_range * pct_remaining**power + lr_end
						return decay / lr_init  # as LambdaLR multiplies by lr_init

		return LambdaLR(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase      )


UpperCamelCase__ :			Union[str, 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:     Union[str, Any] ,   _lowerCamelCase:     int ,   _lowerCamelCase:     Union[str, Any] = None ,   _lowerCamelCase:     Union[str, Any] = None ,   _lowerCamelCase:     List[Any] = None ,   _lowerCamelCase:     Tuple = 1 ,   _lowerCamelCase:     Optional[int] = 1.0 ,   _lowerCamelCase:     int = -1 ,   ):
		__SCREAMING_SNAKE_CASE :	Dict					 =	SchedulerType(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	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      )




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__ :			List[Any]												=				{
    '''configuration_time_series_transformer''': [
        '''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''TimeSeriesTransformerConfig''',
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :			List[Any]												=				[
						    '''TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TimeSeriesTransformerForPrediction''',
						    '''TimeSeriesTransformerModel''',
						    '''TimeSeriesTransformerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_time_series_transformer import (
						    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TimeSeriesTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_time_series_transformer import (
												    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TimeSeriesTransformerForPrediction,
												    TimeSeriesTransformerModel,
												    TimeSeriesTransformerPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :			Dict												=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)