




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__		     =     logging.get_logger(__name__)


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Dict      	=		'linear'
	__magic_name__   :       Dict      	=		'cosine'
	__magic_name__   :       Optional[int]      	=		'cosine_with_restarts'
	__magic_name__   :       List[str]      	=		'polynomial'
	__magic_name__   :       Any      	=		'constant'
	__magic_name__   :       Union[str, Any]      	=		'constant_with_warmup'
	__magic_name__   :       str      	=		'piecewise_constant'






def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int = -1				)     ->						Optional[Any]:
					return LambdaLR(snake_case		,	lambda snake_case				: 1		,	last_epoch=snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int		,	snake_case   :		int = -1				)     ->						List[Any]:

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

					return LambdaLR(snake_case		,	snake_case		,	last_epoch=snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		str		,	snake_case   :		int = -1				)     ->						Union[str, Any]:
					__UpperCamelCase					:	Optional[Any]             =      {}
					__UpperCamelCase					:	int             =      step_rules.split(""","""				)
					for rule_str in rule_list[:-1]:
										__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      rule_str.split(""":"""				)
										__UpperCamelCase					:	int             =      int(snake_case				)
										__UpperCamelCase					:	Union[str, Any]             =      float(snake_case				)
										__UpperCamelCase					:	Optional[int]             =      value
					__UpperCamelCase					:	Dict             =      float(rule_list[-1]				)

					def create_rules_function(snake_case   :		List[str]		,	snake_case   :		Any				):
										def rule_func(snake_case   :		int				) -> float:
															__UpperCamelCase					:	Any             =      sorted(rules_dict.keys()				)
															for i, sorted_step in enumerate(snake_case				):
																				if steps < sorted_step:
																									return rules_dict[sorted_steps[i]]
															return last_lr_multiple

										return rule_func

					__UpperCamelCase					:	Tuple             =      create_rules_function(snake_case		,	snake_case				)

					return LambdaLR(snake_case		,	snake_case		,	last_epoch=snake_case				)





def 				A__ (snake_case   :		int		,	snake_case   :		Optional[Any]		,	snake_case   :		Union[str, Any]		,	snake_case   :		str=-1				)     ->						str:

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

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int		,	snake_case   :		int		,	snake_case   :		float = 0.5		,	snake_case   :		int = -1				)     ->						List[str]:

					def lr_lambda(snake_case   :		Dict				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										__UpperCamelCase					:	Dict             =      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(snake_case				) * 2.0 * progress				))				)

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int		,	snake_case   :		int		,	snake_case   :		int = 1		,	snake_case   :		int = -1				)     ->						Tuple:

					def lr_lambda(snake_case   :		Optional[int]				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										__UpperCamelCase					:	List[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(snake_case				) * progress) % 1.0)				))				)

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)





def 				A__ (snake_case   :		List[str]		,	snake_case   :		Optional[Any]		,	snake_case   :		Optional[int]		,	snake_case   :		str=1e-7		,	snake_case   :		List[str]=1.0		,	snake_case   :		Dict=-1				)     ->						Tuple:
					__UpperCamelCase					:	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(snake_case   :		int				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										elif current_step > num_training_steps:
															return lr_end / lr_init  # as LambdaLR multiplies by lr_init
										else:
															__UpperCamelCase					:	List[str]             =      lr_init - lr_end
															__UpperCamelCase					:	Any             =      num_training_steps - num_warmup_steps
															__UpperCamelCase					:	List[str]             =      1 - (current_step - num_warmup_steps) / decay_steps
															__UpperCamelCase					:	List[Any]             =      lr_range * pct_remaining**power + lr_end
															return decay / lr_init  # as LambdaLR multiplies by lr_init

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)


a__		     =     {
    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__ (snake_case   :		Union[str, SchedulerType]		,	snake_case   :		Optimizer		,	snake_case   :		Optional[str] = None		,	snake_case   :		Optional[int] = None		,	snake_case   :		Optional[int] = None		,	snake_case   :		int = 1		,	snake_case   :		float = 1.0		,	snake_case   :		int = -1		,	)     ->						Dict:
					__UpperCamelCase					:	List[str]             =      SchedulerType(snake_case				)
					__UpperCamelCase					:	int             =      TYPE_TO_SCHEDULER_FUNCTION[name]
					if name == SchedulerType.CONSTANT:
										return schedule_func(snake_case		,	last_epoch=snake_case				)

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

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

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












# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a__		     =     {
    '''configuration_efficientnet''': [
        '''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientNetConfig''',
        '''EfficientNetOnnxConfig''',
    ]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__		     =     ['''EfficientNetImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__		     =     [
	    '''EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''EfficientNetForImageClassification''',
	    '''EfficientNetModel''',
	    '''EfficientNetPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_efficientnet import (
	    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    EfficientNetConfig,
	    EfficientNetOnnxConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_efficientnet import EfficientNetImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_efficientnet import (
		    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    EfficientNetForImageClassification,
		    EfficientNetModel,
		    EfficientNetPreTrainedModel,
		)

else:
	import sys

	a__		     =     _LazyModule(__name__, globals()['''__file__'''], _import_structure)






