
"""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


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   'linear'
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   'cosine'
					_SCREAMING_SNAKE_CASE       :					List[Any]     							=   'cosine_with_restarts'
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   'polynomial'
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   'constant'
					_SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   'constant_with_warmup'
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   'piecewise_constant'




def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = -1 )  ->     Union[str, Any]:







							"""simple docstring"""


							return LambdaLR(SCREAMING_SNAKE_CASE							,    lambda SCREAMING_SNAKE_CASE : 1							,    last_epoch=SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = -1 )  ->     int:







							"""simple docstring"""


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

							return LambdaLR(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    last_epoch=SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = -1 )  ->     Optional[int]:







							"""simple docstring"""


							__snake_case     			=       {}
							__snake_case     			=       step_rules.split("," )
							for rule_str in rule_list[:-1]:
														__snake_case			,   __snake_case     			=       rule_str.split(":" )
														__snake_case     			=       int(SCREAMING_SNAKE_CASE )
														__snake_case     			=       float(SCREAMING_SNAKE_CASE )
														__snake_case     			=       value
							__snake_case     			=       float(rule_list[-1] )

							def create_rules_function(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
														def rule_func(SCREAMING_SNAKE_CASE ) -> float:
																					__snake_case     			=       sorted(rules_dict.keys() )
																					for i, sorted_step in enumerate(SCREAMING_SNAKE_CASE ):
																												if steps < sorted_step:
																																			return rules_dict[sorted_steps[i]]
																					return last_lr_multiple

														return rule_func

							__snake_case     			=       create_rules_function(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							return LambdaLR(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    last_epoch=SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=-1 )  ->     Optional[Any]:







							"""simple docstring"""


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

							return LambdaLR(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = 0.5							,    SCREAMING_SNAKE_CASE = -1 )  ->     int:







							"""simple docstring"""


							def lr_lambda(SCREAMING_SNAKE_CASE ):
														if current_step < num_warmup_steps:
																					return float(SCREAMING_SNAKE_CASE ) / float(max(1							,    SCREAMING_SNAKE_CASE ) )
														__snake_case     			=       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(SCREAMING_SNAKE_CASE ) * 2.0 * progress )) )

							return LambdaLR(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = 1							,    SCREAMING_SNAKE_CASE = -1 )  ->     Any:







							"""simple docstring"""


							def lr_lambda(SCREAMING_SNAKE_CASE ):
														if current_step < num_warmup_steps:
																					return float(SCREAMING_SNAKE_CASE ) / float(max(1							,    SCREAMING_SNAKE_CASE ) )
														__snake_case     			=       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(SCREAMING_SNAKE_CASE ) * progress) % 1.0) )) )

							return LambdaLR(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=1e-7							,    SCREAMING_SNAKE_CASE=1.0							,    SCREAMING_SNAKE_CASE=-1 )  ->     List[Any]:







							"""simple docstring"""


							__snake_case     			=       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(SCREAMING_SNAKE_CASE ):
														if current_step < num_warmup_steps:
																					return float(SCREAMING_SNAKE_CASE ) / float(max(1							,    SCREAMING_SNAKE_CASE ) )
														elif current_step > num_training_steps:
																					return lr_end / lr_init  # as LambdaLR multiplies by lr_init
														else:
																					__snake_case     			=       lr_init - lr_end
																					__snake_case     			=       num_training_steps - num_warmup_steps
																					__snake_case     			=       1 - (current_step - num_warmup_steps) / decay_steps
																					__snake_case     			=       lr_range * pct_remaining**power + lr_end
																					return decay / lr_init  # as LambdaLR multiplies by lr_init

							return LambdaLR(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )


_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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = None							,    SCREAMING_SNAKE_CASE = None							,    SCREAMING_SNAKE_CASE = None							,    SCREAMING_SNAKE_CASE = 1							,    SCREAMING_SNAKE_CASE = 1.0							,    SCREAMING_SNAKE_CASE = -1							,    )  ->     Tuple:







							"""simple docstring"""


							__snake_case     			=       SchedulerType(SCREAMING_SNAKE_CASE )
							__snake_case     			=       TYPE_TO_SCHEDULER_FUNCTION[name]
							if name == SchedulerType.CONSTANT:
														return schedule_func(SCREAMING_SNAKE_CASE							,    last_epoch=SCREAMING_SNAKE_CASE )

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

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

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


"""simple docstring"""







from collections.abc import Sequence
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE = None )  ->     int:







							"""simple docstring"""


							if nums is None or not nums:
														raise ValueError("Input sequence should not be empty" )

							__snake_case     			=       nums[0]
							for i in range(1							,    len(SCREAMING_SNAKE_CASE ) ):
														__snake_case     			=       nums[i]
														__snake_case     			=       max(SCREAMING_SNAKE_CASE							,    ans + num							,    SCREAMING_SNAKE_CASE )

							return ans


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# Try on a sample input from the user
		_SCREAMING_SNAKE_CASE	       =      int(input("""Enter number of elements : """).strip())
		_SCREAMING_SNAKE_CASE	       =      list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
		print(max_subsequence_sum(array))
