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 		a__						(					A__					):
					A	       =							'linear'
					A	       =							'cosine'
					A	       =							'cosine_with_restarts'
					A	       =							'polynomial'
					A	       =							'constant'
					A	       =							'constant_with_warmup'
					A	       =							'piecewise_constant'







def       _snake_case	(   lowerCAmelCase							:   Optimizer   , lowerCAmelCase							:   int = -1	):
		"""simple docstring"""







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




def       _snake_case	(   lowerCAmelCase							:   Optimizer   , lowerCAmelCase							:   int   , 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       _snake_case	(   lowerCAmelCase							:   Optimizer   , lowerCAmelCase							:   str   , lowerCAmelCase							:   int = -1	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    {}
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    step_rules.split(","	)
		for rule_str in rule_list[:-1]:
				SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Tuple				      =    rule_str.split(":"	)
				SCREAMING_SNAKE_CASE_  :		Any				      =    int(lowerCAmelCase	)
				SCREAMING_SNAKE_CASE_  :		str				      =    float(lowerCAmelCase	)
				SCREAMING_SNAKE_CASE_  :		int				      =    value
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    float(rule_list[-1]	)

		def create_rules_function(lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Optional[int]	):
				def rule_func(lowerCAmelCase							:   int	) -> float:
						SCREAMING_SNAKE_CASE_  :		List[str]				      =    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_  :		Optional[Any]				      =    create_rules_function(lowerCAmelCase   , lowerCAmelCase	)

		return LambdaLR(lowerCAmelCase   , lowerCAmelCase   , last_epoch=lowerCAmelCase	)




def       _snake_case	(   lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   Any=-1	):
		"""simple docstring"""







		def lr_lambda(lowerCAmelCase							:   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       _snake_case	(   lowerCAmelCase							:   Optimizer   , lowerCAmelCase							:   int   , lowerCAmelCase							:   int   , lowerCAmelCase							:   float = 0.5   , lowerCAmelCase							:   int = -1	):
		"""simple docstring"""







		def lr_lambda(lowerCAmelCase							:   Dict	):
				if current_step < num_warmup_steps:
						return float(lowerCAmelCase	) / float(max(1   , lowerCAmelCase	)	)
				SCREAMING_SNAKE_CASE_  :		Any				      =    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       _snake_case	(   lowerCAmelCase							:   Optimizer   , lowerCAmelCase							:   int   , lowerCAmelCase							:   int   , lowerCAmelCase							:   int = 1   , lowerCAmelCase							:   int = -1	):
		"""simple docstring"""







		def lr_lambda(lowerCAmelCase							:   Tuple	):
				if current_step < num_warmup_steps:
						return float(lowerCAmelCase	) / float(max(1   , lowerCAmelCase	)	)
				SCREAMING_SNAKE_CASE_  :		Dict				      =    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       _snake_case	(   lowerCAmelCase							:   Dict   , lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   str=1E-7   , lowerCAmelCase							:   Union[str, Any]=1.0   , lowerCAmelCase							:   Any=-1	):
		"""simple docstring"""







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

		return LambdaLR(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase	)


__lowerCamelCase					:	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       _snake_case	(   lowerCAmelCase							:   Union[str, SchedulerType]   , lowerCAmelCase							:   Optimizer   , lowerCAmelCase							:   Optional[str] = None   , lowerCAmelCase							:   Optional[int] = None   , lowerCAmelCase							:   Optional[int] = None   , lowerCAmelCase							:   int = 1   , lowerCAmelCase							:   float = 1.0   , lowerCAmelCase							:   int = -1   , ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Tuple				      =    SchedulerType(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		List[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	)

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase					:	Dict       =  {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	Any       =  [
		    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViTMAEForPreTraining''',
		    '''ViTMAELayer''',
		    '''ViTMAEModel''',
		    '''ViTMAEPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	Optional[Any]       =  [
		    '''TFViTMAEForPreTraining''',
		    '''TFViTMAEModel''',
		    '''TFViTMAEPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_mae import (
				    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMAEForPreTraining,
				    ViTMAELayer,
				    ViTMAEModel,
				    ViTMAEPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
		import sys

		__lowerCamelCase					:	Any       =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
