





'''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  _A (					__lowercase		):
		lowercase__:   List[Any]				     =   '''linear'''
		lowercase__:   Optional[int]				     =   '''cosine'''
		lowercase__:   Tuple				     =   '''cosine_with_restarts'''
		lowercase__:   str				     =   '''polynomial'''
		lowercase__:   Union[str, Any]				     =   '''constant'''
		lowercase__:   Dict				     =   '''constant_with_warmup'''
		lowercase__:   List[str]				     =   '''piecewise_constant'''







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase = -1 ) ->				str:



			"""simple docstring"""


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







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



			"""simple docstring"""


			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 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = -1 ) ->				Optional[Any]:



			"""simple docstring"""


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

			def create_rules_function(_lowerCamelCase    ,	_lowerCamelCase ):
						def rule_func(_lowerCamelCase ) -> float:
									__snake_case					:      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

			__snake_case					:      Any							      =     create_rules_function(_lowerCamelCase    ,	_lowerCamelCase )

			return LambdaLR(_lowerCamelCase    ,	_lowerCamelCase    ,	last_epoch=_lowerCamelCase )







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



			"""simple docstring"""


			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 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = 0.5    ,	_lowerCamelCase = -1 ) ->				List[str]:



			"""simple docstring"""


			def lr_lambda(_lowerCamelCase ):
						if current_step < num_warmup_steps:
									return float(_lowerCamelCase ) / float(max(1    ,	_lowerCamelCase ) )
						__snake_case					:      Optional[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 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = 1    ,	_lowerCamelCase = -1 ) ->				str:



			"""simple docstring"""


			def lr_lambda(_lowerCamelCase ):
						if current_step < num_warmup_steps:
									return float(_lowerCamelCase ) / float(max(1    ,	_lowerCamelCase ) )
						__snake_case					:      str							      =     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 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=1E-7    ,	_lowerCamelCase=1.0    ,	_lowerCamelCase=-1 ) ->				Any:



			"""simple docstring"""


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

			return LambdaLR(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )


__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 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = None    ,	_lowerCamelCase = None    ,	_lowerCamelCase = None    ,	_lowerCamelCase = 1    ,	_lowerCamelCase = 1.0    ,	_lowerCamelCase = -1    ,	) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      str							      =     SchedulerType(_lowerCamelCase )
			__snake_case					:      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       							=				{
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase       							=				[
		    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPSegModel''',
		    '''CLIPSegPreTrainedModel''',
		    '''CLIPSegTextModel''',
		    '''CLIPSegVisionModel''',
		    '''CLIPSegForImageSegmentation''',
		]

if TYPE_CHECKING:
		from .configuration_clipseg import (
		    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPSegConfig,
		    CLIPSegTextConfig,
		    CLIPSegVisionConfig,
		)
		from .processing_clipseg import CLIPSegProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clipseg import (
				    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPSegForImageSegmentation,
				    CLIPSegModel,
				    CLIPSegPreTrainedModel,
				    CLIPSegTextModel,
				    CLIPSegVisionModel,
				)

else:
		import sys

		__UpperCAmelCase       							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)