

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 			A_						(			__snake_case							):
							_UpperCAmelCase       :    List[Any]		 = 'linear'
							_UpperCAmelCase       :    Optional[Any]		 = 'cosine'
							_UpperCAmelCase       :    List[str]		 = 'cosine_with_restarts'
							_UpperCAmelCase       :    Dict		 = 'polynomial'
							_UpperCAmelCase       :    Union[str, Any]		 = 'constant'
							_UpperCAmelCase       :    Any		 = 'constant_with_warmup'
							_UpperCAmelCase       :    Optional[int]		 = 'piecewise_constant'






def   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__      ,					lowerCamelCase__ = -1     )    ->	Dict:
	return LambdaLR(_lowerCAmelCase      ,					lambda lowerCamelCase__     : 1      ,					last_epoch=_lowerCAmelCase     )






def   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__ = -1     )    ->	Any:

	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     )    ->	Union[str, Any]:
	__lowerCamelCase							:							Dict   			=					{}
	__lowerCamelCase							:							Optional[int]   			=					step_rules.split(','     )
	for rule_str in rule_list[:-1]:
		__lowerCamelCase							:							Any   			=					rule_str.split(':'     )
		__lowerCamelCase							:							Optional[Any]   			=					int(_lowerCAmelCase     )
		__lowerCamelCase							:							Tuple   			=					float(_lowerCAmelCase     )
		__lowerCamelCase							:							str   			=					value
	__lowerCamelCase							:							List[Any]   			=					float(rule_list[-1]     )

	def create_rules_function(lowerCamelCase__      ,					lowerCamelCase__     ):
		def rule_func(lowerCamelCase__     ) -> float:
			__lowerCamelCase							:							Tuple   			=					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

	__lowerCamelCase							:							Optional[int]   			=					create_rules_function(_lowerCAmelCase      ,					_lowerCAmelCase     )

	return LambdaLR(_lowerCAmelCase      ,					_lowerCAmelCase      ,					last_epoch=_lowerCAmelCase     )






def   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__=-1     )    ->	Any:

	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     )    ->	Any:

	def lr_lambda(lowerCamelCase__     ):
		if current_step < num_warmup_steps:
			return float(_lowerCAmelCase     ) / float(max(1      ,					_lowerCAmelCase     )     )
		__lowerCamelCase							:							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   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__ = 1      ,					lowerCamelCase__ = -1     )    ->	int:

	def lr_lambda(lowerCamelCase__     ):
		if current_step < num_warmup_steps:
			return float(_lowerCAmelCase     ) / float(max(1      ,					_lowerCAmelCase     )     )
		__lowerCamelCase							:							Tuple   			=					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     )    ->	str:
	__lowerCamelCase							:							List[str]   			=					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:
			__lowerCamelCase							:							Tuple   			=					lr_init - lr_end
			__lowerCamelCase							:							Optional[int]   			=					num_training_steps - num_warmup_steps
			__lowerCamelCase							:							int   			=					1 - (current_step - num_warmup_steps) / decay_steps
			__lowerCamelCase							:							Tuple   			=					lr_range * pct_remaining**power + lr_end
			return decay / lr_init  # as LambdaLR multiplies by lr_init

	return LambdaLR(_lowerCAmelCase      ,					_lowerCAmelCase      ,					_lowerCAmelCase     )


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   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__ = None      ,					lowerCamelCase__ = None      ,					lowerCamelCase__ = None      ,					lowerCamelCase__ = 1      ,					lowerCamelCase__ = 1.0      ,					lowerCamelCase__ = -1      ,					)    ->	Dict:
	__lowerCamelCase							:							List[str]   			=					SchedulerType(_lowerCAmelCase     )
	__lowerCamelCase							:							Optional[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     )











__lowerCamelCase			:	Any					    =	9.8_0_6_6_5



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase = g	)		->					float:

 if fluid_density <= 0:
  raise ValueError("Impossible fluid density"	)
 if volume < 0:
  raise ValueError("Impossible Object volume"	)
 if gravity <= 0:
  raise ValueError("Impossible Gravity"	)

 return fluid_density * gravity * volume


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()
