





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   A							(						__lowercase    ):
      _snake_case				  =42
      _snake_case				  =42
      _snake_case				  =None

class   A							(						__lowercase		,   __lowercase    ):
      _snake_case				  =2



      @register_to_config
      def __init__(						self:    List[str]		,	_lowerCAmelCase:    float = 0.02		,	_lowerCAmelCase:    float = 100		,	_lowerCAmelCase:    float = 1.0_07		,	_lowerCAmelCase:    float = 80		,	_lowerCAmelCase:    float = 0.05		,	_lowerCAmelCase:    float = 50		,	)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_           =sigma_max

         # setable values
         UpperCAmelCase_           =None
         UpperCAmelCase_           =None
         UpperCAmelCase_           =None  # sigma(t_i)



      def 						lowerCAmelCase__     (						self:    Any		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    Optional[int] = None							)    ->     torch.FloatTensor:
         '''simple docstring'''

         return sample



      def 						lowerCAmelCase__     (						self:    List[Any]		,	_lowerCAmelCase:    int		,	_lowerCAmelCase:    Union[str, torch.device] = None							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =num_inference_steps
         UpperCAmelCase_           =np.arange(0		,	self.num_inference_steps							)[::-1].copy()
         UpperCAmelCase_           =torch.from_numpy(_lowerCAmelCase							).to(_lowerCAmelCase							)
         UpperCAmelCase_           =[
             (
                 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
         ]
         UpperCAmelCase_           =torch.tensor(_lowerCAmelCase		,	dtype=torch.floataa		,	device=_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    List[str]		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    float		,	_lowerCAmelCase:    Optional[torch.Generator] = None							)    ->     Tuple[torch.FloatTensor, float]:
         '''simple docstring'''

         if self.config.s_min <= sigma <= self.config.s_max:
            UpperCAmelCase_           =min(self.config.s_churn / self.num_inference_steps		,	2**0.5 - 1							)
         else:
            UpperCAmelCase_           =0

         # sample eps ~ N(0, S_noise^2 * I)
         UpperCAmelCase_           =self.config.s_noise * randn_tensor(sample.shape		,	generator=_lowerCAmelCase							).to(sample.device							)
         UpperCAmelCase_           =sigma + gamma * sigma
         UpperCAmelCase_           =sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

         return sample_hat, sigma_hat



      def 						lowerCAmelCase__     (						self:    Optional[int]		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    float		,	_lowerCAmelCase:    float		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    bool = True		,	)    ->     Union[KarrasVeOutput, Tuple]:
         '''simple docstring'''

         UpperCAmelCase_           =sample_hat + sigma_hat * model_output
         UpperCAmelCase_           =(sample_hat - pred_original_sample) / sigma_hat
         UpperCAmelCase_           =sample_hat + (sigma_prev - sigma_hat) * derivative

         if not return_dict:
            return (sample_prev, derivative)

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



      def 						lowerCAmelCase__     (						self:    Tuple		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    float		,	_lowerCAmelCase:    float		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    bool = True		,	)    ->     Union[KarrasVeOutput, Tuple]:
         '''simple docstring'''

         UpperCAmelCase_           =sample_prev + sigma_prev * model_output
         UpperCAmelCase_           =(sample_prev - pred_original_sample) / sigma_prev
         UpperCAmelCase_           =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=_lowerCAmelCase		,	derivative=_lowerCAmelCase		,	pred_original_sample=_lowerCAmelCase							)






      def 						lowerCAmelCase__     (						self:    int		,	_lowerCAmelCase:    Dict		,	_lowerCAmelCase:    List[str]		,	_lowerCAmelCase:    Any							)    ->     List[Any]:
         '''simple docstring'''

         raise NotImplementedError()








'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase = True   ,   __lowercase = False    ):

  """simple docstring"""







  __A	:		List[str]	  =      scheduler
  __A	:		Dict	  =      optimizers if isinstance(__lowercase   ,   (list, tuple)    ) else [optimizers]
  __A	:		List[Any]	  =      split_batches
  __A	:		Any	  =      step_with_optimizer
  __A	:		List[Any]	  =      GradientState()


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  if not self.step_with_optimizer:
   # No link between scheduler and optimizer -> just step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
   return

  # Otherwise, first make sure the optimizer was stepped.
  if not self.gradient_state.sync_gradients:
   if self.gradient_state.adjust_scheduler:
    self.scheduler._step_count += 1
   return

  for opt in self.optimizers:
   if opt.step_was_skipped:
    return
  if self.split_batches:
   # Split batches -> the training dataloader batch size is not changed so one step per training step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
  else:
   # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
   # num_processes steps per training step
   __A	:		Tuple	  =      AcceleratorState().num_processes
   for _ in range(__lowercase    ):
    # Special case when using OneCycle and `drop_last` was not used
    if hasattr(self.scheduler   ,   'total_steps'    ):
     if self.scheduler._step_count <= self.scheduler.total_steps:
      self.scheduler.step(*__lowercase   ,   **__lowercase    )
    else:
     self.scheduler.step(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_last_lr()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.state_dict()


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  self.scheduler.load_state_dict(__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_lr()







 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  return self.scheduler.print_lr(*__lowercase   ,   **__lowercase    )






