
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      :				Dict          = logging.get_logger(__name__)


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Union[str, Any]			  =       "linear"
		__lowercase : Tuple			  =       "cosine"
		__lowercase : Tuple			  =       "cosine_with_restarts"
		__lowercase : int			  =       "polynomial"
		__lowercase : Optional[int]			  =       "constant"
		__lowercase : Optional[Any]			  =       "constant_with_warmup"
		__lowercase : List[Any]			  =       "piecewise_constant"






def    A (      lowercase					, lowercase = -1				)     ->						Optional[Any]:
		'''simple docstring'''

		return LambdaLR(lowercase					, lambda lowercase				: 1					, last_epoch=lowercase				)


def    A (      lowercase					, lowercase					, lowercase = -1				)     ->						List[str]:
		'''simple docstring'''

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

		return LambdaLR(lowercase					, lowercase					, last_epoch=lowercase				)


def    A (      lowercase					, lowercase					, lowercase = -1				)     ->						List[str]:
		'''simple docstring'''

		UpperCamelCase						=  {}
		UpperCamelCase						=  step_rules.split(','				)
		for rule_str in rule_list[:-1]:
				UpperCamelCase			,						UpperCamelCase						=  rule_str.split(':'				)
				UpperCamelCase						=  int(lowercase				)
				UpperCamelCase						=  float(lowercase				)
				UpperCamelCase						=  value
		UpperCamelCase						=  float(rule_list[-1]				)

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

				return rule_func

		UpperCamelCase						=  create_rules_function(lowercase					, lowercase				)

		return LambdaLR(lowercase					, lowercase					, last_epoch=lowercase				)


def    A (      lowercase					, lowercase					, lowercase					, lowercase=-1				)     ->						Any:
		'''simple docstring'''

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

		return LambdaLR(lowercase					, lowercase					, lowercase				)


def    A (      lowercase					, lowercase					, lowercase					, lowercase = 0.5					, lowercase = -1				)     ->						Optional[Any]:
		'''simple docstring'''

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

		return LambdaLR(lowercase					, lowercase					, lowercase				)


def    A (      lowercase					, lowercase					, lowercase					, lowercase = 1					, lowercase = -1				)     ->						List[Any]:
		'''simple docstring'''

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

		return LambdaLR(lowercase					, lowercase					, lowercase				)


def    A (      lowercase					, lowercase					, lowercase					, lowercase=1e-7					, lowercase=1.0					, lowercase=-1				)     ->						Dict:
		'''simple docstring'''

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

		return LambdaLR(lowercase					, lowercase					, lowercase				)


_UpperCAmelCase      :				Optional[int]          = {
    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 (      lowercase					, lowercase					, lowercase = None					, lowercase = None					, lowercase = None					, lowercase = 1					, lowercase = 1.0					, lowercase = -1					, )     ->						Union[str, Any]:
		'''simple docstring'''

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

		if name == SchedulerType.PIECEWISE_CONSTANT:
				return schedule_func(lowercase					, step_rules=lowercase					, last_epoch=lowercase				)

		# 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(lowercase					, num_warmup_steps=lowercase					, last_epoch=lowercase				)

		# 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(
				    lowercase					, num_warmup_steps=lowercase					, num_training_steps=lowercase					, num_cycles=lowercase					, last_epoch=lowercase					, )

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

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


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		if a < 0 or b < 0:
				raise ValueError('the value of both inputs must be positive'				)

		UpperCamelCase						=  str(bin(lowercase				)				)[2:]  # remove the leading "0b"
		UpperCamelCase						=  str(bin(lowercase				)				)[2:]  # remove the leading "0b"

		UpperCamelCase						=  max(len(lowercase				)					, len(lowercase				)				)

		return "0b" + "".join(
		    str(int(char_a != char_b				)				)
		    for char_a, char_b in zip(a_binary.zfill(lowercase				)					, b_binary.zfill(lowercase				)				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
