






"""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       :	Tuple             = logging.get_logger(__name__)







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





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





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ = -1):
					return LambdaLR(lowerCamelCase_ ,     lambda lowerCamelCase_: 1 ,     last_epoch=lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ = -1):
					a__          =  {}
					a__          =  step_rules.split(''',''')
					for rule_str in rule_list[:-1]:
										a__       ,a__          =  rule_str.split(''':''')
										a__          =  int(lowerCamelCase_)
										a__          =  float(lowerCamelCase_)
										a__          =  value
					a__          =  float(rule_list[-1])

					def create_rules_function(lowerCamelCase_ ,     lowerCamelCase_):
										def rule_func(lowerCamelCase_) -> float:
															a__          =  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

					a__          =  create_rules_function(lowerCamelCase_ ,     lowerCamelCase_)

					return LambdaLR(lowerCamelCase_ ,     lowerCamelCase_ ,     last_epoch=lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      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        SCREAMING_SNAKE_CASE       (      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_))
										a__          =  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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ = 1 ,     lowerCamelCase_ = -1):

					def lr_lambda(lowerCamelCase_):
										if current_step < num_warmup_steps:
															return float(lowerCamelCase_) / float(max(1 ,     lowerCamelCase_))
										a__          =  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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=1E-7 ,     lowerCamelCase_=1.0 ,     lowerCamelCase_=-1):
					a__          =  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:
															a__          =  lr_init - lr_end
															a__          =  num_training_steps - num_warmup_steps
															a__          =  1 - (current_step - num_warmup_steps) / decay_steps
															a__          =  lr_range * pct_remaining**power + lr_end
															return decay / lr_init  # as LambdaLR multiplies by lr_init

					return LambdaLR(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)


__a       :	Tuple             = {
    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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ = None ,     lowerCamelCase_ = None ,     lowerCamelCase_ = None ,     lowerCamelCase_ = 1 ,     lowerCamelCase_ = 1.0 ,     lowerCamelCase_ = -1 ,     ):
					a__          =  SchedulerType(lowerCamelCase_)
					a__          =  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,
)


__a       :	str             = {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	int             = [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

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


