



"""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


lowercase__		:				Optional[Any]          =	logging.get_logger(__name__)







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """linear"""
      _SCREAMING_SNAKE_CASE  				=     """cosine"""
      _SCREAMING_SNAKE_CASE  				=     """cosine_with_restarts"""
      _SCREAMING_SNAKE_CASE  				=     """polynomial"""
      _SCREAMING_SNAKE_CASE  				=     """constant"""
      _SCREAMING_SNAKE_CASE  				=     """constant_with_warmup"""
      _SCREAMING_SNAKE_CASE  				=     """piecewise_constant"""




def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int = -1						)						->   Tuple:

  """simple docstring"""




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

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int = -1						)						->   str:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				int						):
    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 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				int = -1						)						->   int:

  """simple docstring"""




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

  def create_rules_function(lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				int						):
    def rule_func(lowerCAmelCase__      :				int						) -> float:
      lowerCAmelCase_   :				str								= 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_   :				Dict								= create_rules_function(lowerCAmelCase__    ,   lowerCAmelCase__						)

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Union[str, Any]=-1						)						->   Any:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				int						):
    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 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				float = 0.5    ,   lowerCAmelCase__      :				int = -1						)						->   Union[str, Any]:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				Any						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1    ,   lowerCAmelCase__						)						)
    lowerCAmelCase_   :				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 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int = 1    ,   lowerCAmelCase__      :				int = -1						)						->   int:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				str						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1    ,   lowerCAmelCase__						)						)
    lowerCAmelCase_   :				Dict								= 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 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Tuple=1e-7    ,   lowerCAmelCase__      :				Union[str, Any]=1.0    ,   lowerCAmelCase__      :				int=-1						)						->   Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_   :				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(lowerCAmelCase__      :				int						):
    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_   :				Any								= lr_init - lr_end
      lowerCAmelCase_   :				int								= num_training_steps - num_warmup_steps
      lowerCAmelCase_   :				Dict								= 1 - (current_step - num_warmup_steps) / decay_steps
      lowerCAmelCase_   :				Dict								= lr_range * pct_remaining**power + lr_end
      return decay / lr_init  # as LambdaLR multiplies by lr_init

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)


lowercase__		:				Any          =	{
    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_    (    lowerCAmelCase__      :				Union[str, SchedulerType]    ,   lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				Optional[str] = None    ,   lowerCAmelCase__      :				Optional[int] = None    ,   lowerCAmelCase__      :				Optional[int] = None    ,   lowerCAmelCase__      :				int = 1    ,   lowerCAmelCase__      :				float = 1.0    ,   lowerCAmelCase__      :				int = -1    ,   )						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				List[str]								= SchedulerType(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[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__						)










"""simple docstring"""







import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    """The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionImg2ImgPipeline` instead."""
)





