
"""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__      :    Tuple    							= logging.get_logger(__name__)



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



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






def lowercase_  (   _snake_case   ,_snake_case = -1						):
							return LambdaLR(_snake_case   ,lambda _snake_case						: 1   ,last_epoch=_snake_case						)

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case = -1						):

							def lr_lambda(_snake_case						):
														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 lowercase_  (   _snake_case   ,_snake_case   ,_snake_case = -1						):
							SCREAMING_SNAKE_CASE__						:       Any           =   {}
							SCREAMING_SNAKE_CASE__						:       int           =   step_rules.split(""","""						)
							for rule_str in rule_list[:-1]:
														SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   rule_str.split(""":"""						)
														SCREAMING_SNAKE_CASE__						:       Dict           =   int(_snake_case						)
														SCREAMING_SNAKE_CASE__						:       str           =   float(_snake_case						)
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   value
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   float(rule_list[-1]						)

							def create_rules_function(_snake_case   ,_snake_case						):
														def rule_func(_snake_case						) -> float:
																					SCREAMING_SNAKE_CASE__						:       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

							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   create_rules_function(_snake_case   ,_snake_case						)

							return LambdaLR(_snake_case   ,_snake_case   ,last_epoch=_snake_case						)

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case=-1						):

							def lr_lambda(_snake_case						):
														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 lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case = 0.5   ,_snake_case = -1						):

							def lr_lambda(_snake_case						):
														if current_step < num_warmup_steps:
																					return float(_snake_case						) / float(max(1   ,_snake_case						)						)
														SCREAMING_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(_snake_case						) * 2.0 * progress						))						)

							return LambdaLR(_snake_case   ,_snake_case   ,_snake_case						)

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case = 1   ,_snake_case = -1						):

							def lr_lambda(_snake_case						):
														if current_step < num_warmup_steps:
																					return float(_snake_case						) / float(max(1   ,_snake_case						)						)
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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 lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case=1E-7   ,_snake_case=1.0   ,_snake_case=-1						):
							SCREAMING_SNAKE_CASE__						:       Any           =   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						):
														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:
																					SCREAMING_SNAKE_CASE__						:       int           =   lr_init - lr_end
																					SCREAMING_SNAKE_CASE__						:       List[str]           =   num_training_steps - num_warmup_steps
																					SCREAMING_SNAKE_CASE__						:       Any           =   1 - (current_step - num_warmup_steps) / decay_steps
																					SCREAMING_SNAKE_CASE__						:       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						)


UpperCAmelCase__      :    Dict    							= {
    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 lowercase_  (   _snake_case   ,_snake_case   ,_snake_case = None   ,_snake_case = None   ,_snake_case = None   ,_snake_case = 1   ,_snake_case = 1.0   ,_snake_case = -1   ,):
							SCREAMING_SNAKE_CASE__						:       Dict           =   SchedulerType(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       List[str]           =   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						)






"""simple docstring"""




def lowercase_  (   _snake_case						):
							if a < 0:
														raise ValueError("""Input value must be a positive integer"""						)
							elif isinstance(_snake_case   ,_snake_case						):
														raise TypeError("""Input value must be a 'int' type"""						)
							return bin(_snake_case						).count("""1"""						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




