





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_					:   Any 							=						logging.get_logger(__name__)

class      lowercase__    (							lowercase__  ):
  __UpperCamelCase	     =  """linear"""
  __UpperCamelCase	     =  """cosine"""
  __UpperCamelCase	     =  """cosine_with_restarts"""
  __UpperCamelCase	     =  """polynomial"""
  __UpperCamelCase	     =  """constant"""
  __UpperCamelCase	     =  """constant_with_warmup"""
  __UpperCamelCase	     =  """piecewise_constant"""






def 							_lowerCAmelCase (  _a   :					Optimizer					,   _a   :					int = -1			)	->			Any:
     return LambdaLR(_lowerCamelCase					,   lambda _a			: 1					,   last_epoch=_lowerCamelCase			)






def 							_lowerCAmelCase (  _a   :					Optimizer					,   _a   :					int					,   _a   :					int = -1			)	->			Any:


     def lr_lambda(_a   :					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 							_lowerCAmelCase (  _a   :					Optimizer					,   _a   :					str					,   _a   :					int = -1			)	->			List[Any]:
     lowerCAmelCase_					:       List[Any]    =		{}
     lowerCAmelCase_					:       List[str]    =		step_rules.split(""","""			)
     for rule_str in rule_list[:-1]:
          lowerCAmelCase_					:       str    =		rule_str.split(""":"""			)
          lowerCAmelCase_					:       Dict    =		int(_lowerCamelCase			)
          lowerCAmelCase_					:       Optional[Any]    =		float(_lowerCamelCase			)
          lowerCAmelCase_					:       List[Any]    =		value
     lowerCAmelCase_					:       str    =		float(rule_list[-1]			)

     def create_rules_function(_a   :					Any					,   _a   :					List[Any]			):
          def rule_func(_a   :					int			) -> float:
               lowerCAmelCase_					:       Optional[Any]    =		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_					:       List[Any]    =		create_rules_function(_lowerCamelCase					,   _lowerCamelCase			)

     return LambdaLR(_lowerCamelCase					,   _lowerCamelCase					,   last_epoch=_lowerCamelCase			)






def 							_lowerCAmelCase (  _a   :					int					,   _a   :					str					,   _a   :					Union[str, Any]					,   _a   :					Union[str, Any]=-1			)	->			Optional[Any]:


     def lr_lambda(_a   :					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 							_lowerCAmelCase (  _a   :					Optimizer					,   _a   :					int					,   _a   :					int					,   _a   :					float = 0.5					,   _a   :					int = -1			)	->			Optional[Any]:


     def lr_lambda(_a   :					Union[str, Any]			):
          if current_step < num_warmup_steps:
               return float(_lowerCamelCase			) / float(max(1					,   _lowerCamelCase			)			)
          lowerCAmelCase_					:       str    =		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 (  _a   :					Optimizer					,   _a   :					int					,   _a   :					int					,   _a   :					int = 1					,   _a   :					int = -1			)	->			Optional[int]:


     def lr_lambda(_a   :					List[str]			):
          if current_step < num_warmup_steps:
               return float(_lowerCamelCase			) / float(max(1					,   _lowerCamelCase			)			)
          lowerCAmelCase_					:       Tuple    =		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 (  _a   :					str					,   _a   :					Any					,   _a   :					Optional[int]					,   _a   :					Any=1E-7					,   _a   :					Any=1.0					,   _a   :					int=-1			)	->			Dict:
     lowerCAmelCase_					:       str    =		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(_a   :					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_					:       Any    =		num_training_steps - num_warmup_steps
               lowerCAmelCase_					:       str    =		1 - (current_step - num_warmup_steps) / decay_steps
               lowerCAmelCase_					:       List[Any]    =		lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(_lowerCamelCase					,   _lowerCamelCase					,   _lowerCamelCase			)


UpperCAmelCase_					:   Union[str, 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 							_lowerCAmelCase (  _a   :					Union[str, SchedulerType]					,   _a   :					Optimizer					,   _a   :					Optional[str] = None					,   _a   :					Optional[int] = None					,   _a   :					Optional[int] = None					,   _a   :					int = 1					,   _a   :					float = 1.0					,   _a   :					int = -1					,   )	->			List[Any]:
     lowerCAmelCase_					:       List[str]    =		SchedulerType(_lowerCamelCase			)
     lowerCAmelCase_					:       Optional[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			)







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  None





class      lowercase__    (							__A     ,					__A  ):
  __UpperCamelCase	     =  2







  @register_to_config
  def __init__(	self    ,						_lowercase = 0.02    ,						_lowercase = 100    ,						_lowercase = 1.007    ,						_lowercase = 80    ,						_lowercase = 0.05    ,						_lowercase = 50    ,						):
       # standard deviation of the initial noise distribution
       lowerCAmelCase_					:       int    =		sigma_max

       # setable values
       lowerCAmelCase_					:       int    =		None
       lowerCAmelCase_					:       np.IntTensor    =		None
       lowerCAmelCase_					:       torch.FloatTensor    =		None  # sigma(t_i)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       return sample







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       int    =		num_inference_steps
       lowerCAmelCase_					:       int    =		np.arange(0    ,						self.num_inference_steps						)[::-1].copy()
       lowerCAmelCase_					:       List[Any]    =		torch.from_numpy(_lowercase						).to(_lowercase						)
       lowerCAmelCase_					:       List[str]    =		[
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor(_lowercase    ,						dtype=torch.floataa    ,						device=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase = None						):
       if self.config.s_min <= sigma <= self.config.s_max:
            lowerCAmelCase_					:       List[Any]    =		min(self.config.s_churn / self.num_inference_steps    ,						2**0.5 - 1						)
       else:
            lowerCAmelCase_					:       List[str]    =		0

       # sample eps ~ N(0, S_noise^2 * I)
       lowerCAmelCase_					:       List[Any]    =		self.config.s_noise * randn_tensor(sample.shape    ,						generator=_lowercase						).to(sample.device						)
       lowerCAmelCase_					:       Optional[int]    =		sigma + gamma * sigma
       lowerCAmelCase_					:       int    =		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       Union[str, Any]    =		sample_hat + sigma_hat * model_output
       lowerCAmelCase_					:       Dict    =		(sample_hat - pred_original_sample) / sigma_hat
       lowerCAmelCase_					:       List[Any]    =		sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       str    =		sample_prev + sigma_prev * model_output
       lowerCAmelCase_					:       List[Any]    =		(sample_prev - pred_original_sample) / sigma_prev
       lowerCAmelCase_					:       Tuple    =		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       raise NotImplementedError()
