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


SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  "linear"
				_snake_case     =  "cosine"
				_snake_case     =  "cosine_with_restarts"
				_snake_case     =  "polynomial"
				_snake_case     =  "constant"
				_snake_case     =  "constant_with_warmup"
				_snake_case     =  "piecewise_constant"



def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase = -1			):






	"""simple docstring"""





	return LambdaLR(lowerCamelCase_     ,lambda __UpperCAmelCase			: 1     ,last_epoch=lowerCamelCase_			)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase = -1			):






	"""simple docstring"""





	def lr_lambda(__UpperCAmelCase			):
		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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase = -1			):






	"""simple docstring"""





	_lowercase :    Union[str, Any]							=							{}
	_lowercase :    List[str]							=							step_rules.split(','			)
	for rule_str in rule_list[:-1]:
		_lowercase :    str							=							rule_str.split(':'			)
		_lowercase :    Optional[int]							=							int(lowerCamelCase_			)
		_lowercase :    Optional[int]							=							float(lowerCamelCase_			)
		_lowercase :    Optional[int]							=							value
	_lowercase :    str							=							float(rule_list[-1]			)

	def create_rules_function(__UpperCAmelCase     ,__UpperCAmelCase			):
		def rule_func(__UpperCAmelCase			) -> float:
			_lowercase :    Dict							=							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

	_lowercase :    str							=							create_rules_function(lowerCamelCase_     ,lowerCamelCase_			)

	return LambdaLR(lowerCamelCase_     ,lowerCamelCase_     ,last_epoch=lowerCamelCase_			)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase=-1			):






	"""simple docstring"""





	def lr_lambda(__UpperCAmelCase			):
		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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase = 0.5     ,__UpperCAmelCase = -1			):






	"""simple docstring"""





	def lr_lambda(__UpperCAmelCase			):
		if current_step < num_warmup_steps:
			return float(lowerCamelCase_			) / float(max(1     ,lowerCamelCase_			)			)
		_lowercase :    List[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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase = 1     ,__UpperCAmelCase = -1			):






	"""simple docstring"""





	def lr_lambda(__UpperCAmelCase			):
		if current_step < num_warmup_steps:
			return float(lowerCamelCase_			) / float(max(1     ,lowerCamelCase_			)			)
		_lowercase :    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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase=1E-7     ,__UpperCAmelCase=1.0     ,__UpperCAmelCase=-1			):






	"""simple docstring"""





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

	return LambdaLR(lowerCamelCase_     ,lowerCamelCase_     ,lowerCamelCase_			)


SCREAMING_SNAKE_CASE									=						{
    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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase = None     ,__UpperCAmelCase = None     ,__UpperCAmelCase = None     ,__UpperCAmelCase = 1     ,__UpperCAmelCase = 1.0     ,__UpperCAmelCase = -1     ,):






	"""simple docstring"""





	_lowercase :    Union[str, Any]							=							SchedulerType(lowerCamelCase_			)
	_lowercase :    Tuple							=							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"""







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							len(__UpperCAmelCase			)
	_lowercase :    Any							=							len(__UpperCAmelCase			)
	_lowercase :    List[Any]							=							[[False for _ in range(m + 1			)] for _ in range(n + 1			)]
	_lowercase :    Union[str, Any]							=							True
	for i in range(__UpperCAmelCase			):
		for j in range(m + 1			):
			if dp[i][j]:
				if j < m and a[i].upper() == b[j]:
					_lowercase :    Optional[int]							=							True
				if a[i].islower():
					_lowercase :    Union[str, Any]							=							True
	return dp[n][m]


if __name__ == "__main__":
						import doctest

						doctest.testmod()



