




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

import numpy as np
import torch

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


_UpperCAmelCase						:    Dict      =  logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class  __lowerCAmelCase							(				__lowerCAmelCase):
       _a			       =    42
       _a			       =    None







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase=0.999,      lowerCamelCase="cosine",      ):
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowerCamelCase ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(lowerCamelCase ):
      return math.exp(t * -12.0 )

  else:
    raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}" )

  lowercase     :int   						=    []
  for i in range(__A ):
    lowercase     :Optional[int]   						=    i / num_diffusion_timesteps
    lowercase     :int   						=    (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(__A ) / alpha_bar_fn(__A ),      __A ) )
  return torch.tensor(__A,      dtype=torch.floataa )







class  __lowerCAmelCase							(				__lowerCAmelCase   ,			__lowerCAmelCase):
       _a			       =    [e.name for e in KarrasDiffusionSchedulers]
       _a			       =    1






       @register_to_config
       def __init__( self:       Tuple							,       _lowerCAmelCase:       int = 10_00							,       _lowerCAmelCase:       float = 0.00_01							,       _lowerCAmelCase:       float = 0.02							,       _lowerCAmelCase:       str = "linear"							,       _lowerCAmelCase:       Optional[Union[np.ndarray, List[float]]] = None							,       _lowerCAmelCase:       str = "epsilon"							,       _lowerCAmelCase:       str = "linspace"							,       _lowerCAmelCase:       int = 0							,       ):


         if trained_betas is not None:
           lowercase     :List[str]   						=    torch.tensor(_UpperCamelCase							,       dtype=torch.floataa					)
         elif beta_schedule == "linear":
           lowercase     :List[str]   						=    torch.linspace(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       dtype=torch.floataa					)
         elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           lowercase     :int   						=    (
               torch.linspace(beta_start**0.5							,       beta_end**0.5							,       _UpperCamelCase							,       dtype=torch.floataa					) ** 2
           )
         elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           lowercase     :Any   						=    betas_for_alpha_bar(_UpperCamelCase					)
         else:
           raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"					)

         lowercase     :Tuple   						=    1.0 - self.betas
         lowercase     :int   						=    torch.cumprod(self.alphas							,       dim=0					)

         lowercase     :Tuple   						=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
         lowercase     :Dict   						=    np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
         lowercase     :Tuple   						=    torch.from_numpy(_UpperCamelCase					)

         # setable values
         lowercase     :List[str]   						=    None
         lowercase     :int   						=    np.linspace(0							,       num_train_timesteps - 1							,       _UpperCamelCase							,       dtype=_UpperCamelCase					)[::-1].copy()
         lowercase     :Optional[Any]   						=    torch.from_numpy(_UpperCamelCase					)
         lowercase     :Tuple   						=    False






       @property
       def        SCREAMING_SNAKE_CASE     ( self:       List[str]					):


         if self.config.timestep_spacing in ["linspace", "trailing"]:
           return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5






       def        SCREAMING_SNAKE_CASE     ( self:       int							,       _lowerCAmelCase:       torch.FloatTensor							,       _lowerCAmelCase:       Union[float, torch.FloatTensor]					):


         if isinstance(_UpperCamelCase							,       torch.Tensor					):
           lowercase     :Optional[int]   						=    timestep.to(self.timesteps.device					)
         lowercase     :Tuple   						=    (self.timesteps == timestep).nonzero().item()
         lowercase     :str   						=    self.sigmas[step_index]
         lowercase     :List[str]   						=    sample / ((sigma**2 + 1) ** 0.5)
         lowercase     :str   						=    True
         return sample






       def        SCREAMING_SNAKE_CASE     ( self:       Tuple							,       _lowerCAmelCase:       int							,       _lowerCAmelCase:       Union[str, torch.device] = None					):
         lowercase     :Union[str, Any]   						=    num_inference_steps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
           lowercase     :Any   						=    np.linspace(0							,       self.config.num_train_timesteps - 1							,       _UpperCamelCase							,       dtype=_UpperCamelCase					)[
               ::-1
           ].copy()
         elif self.config.timestep_spacing == "leading":
           lowercase     :Tuple   						=    self.config.num_train_timesteps // self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           lowercase     :List[Any]   						=    (np.arange(0							,       _UpperCamelCase					) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase					)
           timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
           lowercase     :List[str]   						=    self.config.num_train_timesteps / self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           lowercase     :str   						=    (np.arange(self.config.num_train_timesteps							,       0							,       -step_ratio					)).round().copy().astype(_UpperCamelCase					)
           timesteps -= 1
         else:
           raise ValueError(
               F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."					)

         lowercase     :Tuple   						=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
         lowercase     :Union[str, Any]   						=    np.interp(_UpperCamelCase							,       np.arange(0							,       len(_UpperCamelCase					)					)							,       _UpperCamelCase					)
         lowercase     :Optional[int]   						=    np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
         lowercase     :Dict   						=    torch.from_numpy(_UpperCamelCase					).to(device=_UpperCamelCase					)
         if str(_UpperCamelCase					).startswith("mps"					):
           # mps does not support float64
           lowercase     :List[str]   						=    torch.from_numpy(_UpperCamelCase					).to(_UpperCamelCase							,       dtype=torch.floataa					)
         else:
           lowercase     :Any   						=    torch.from_numpy(_UpperCamelCase					).to(device=_UpperCamelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       torch.FloatTensor							,       _lowerCAmelCase:       Union[float, torch.FloatTensor]							,       _lowerCAmelCase:       torch.FloatTensor							,       _lowerCAmelCase:       Optional[torch.Generator] = None							,       _lowerCAmelCase:       bool = True							,       ):


         if (
             isinstance(_UpperCamelCase							,       _UpperCamelCase					)
             or isinstance(_UpperCamelCase							,       torch.IntTensor					)
             or isinstance(_UpperCamelCase							,       torch.LongTensor					)
         ):
           raise ValueError(
               (
                   "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                   " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                   " one of the `scheduler.timesteps` as a timestep."
               )							,       )

         if not self.is_scale_input_called:
           logger.warning(
               "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
               "See `StableDiffusionPipeline` for a usage example."					)

         if isinstance(_UpperCamelCase							,       torch.Tensor					):
           lowercase     :Optional[Any]   						=    timestep.to(self.timesteps.device					)

         lowercase     :Optional[int]   						=    (self.timesteps == timestep).nonzero().item()
         lowercase     :Union[str, Any]   						=    self.sigmas[step_index]

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
           lowercase     :int   						=    sample - sigma * model_output
         elif self.config.prediction_type == "v_prediction":
           # * c_out + input * c_skip
           lowercase     :Optional[Any]   						=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         elif self.config.prediction_type == "sample":
           raise NotImplementedError("prediction_type not implemented yet: sample"					)
         else:
           raise ValueError(
               F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"					)

         lowercase     :Tuple   						=    self.sigmas[step_index]
         lowercase     :int   						=    self.sigmas[step_index + 1]
         lowercase     :Tuple   						=    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
         lowercase     :List[Any]   						=    (sigma_to**2 - sigma_up**2) ** 0.5

         # 2. Convert to an ODE derivative
         lowercase     :List[Any]   						=    (sample - pred_original_sample) / sigma

         lowercase     :Tuple   						=    sigma_down - sigma

         lowercase     :Optional[int]   						=    sample + derivative * dt

         lowercase     :int   						=    model_output.device
         lowercase     :List[Any]   						=    randn_tensor(model_output.shape							,       dtype=model_output.dtype							,       device=_UpperCamelCase							,       generator=_UpperCamelCase					)

         lowercase     :List[Any]   						=    prev_sample + noise * sigma_up

         if not return_dict:
           return (prev_sample,)

         return EulerAncestralDiscreteSchedulerOutput(
             prev_sample=_UpperCamelCase							,       pred_original_sample=_UpperCamelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Optional[Any]							,       _lowerCAmelCase:       torch.FloatTensor							,       _lowerCAmelCase:       torch.FloatTensor							,       _lowerCAmelCase:       torch.FloatTensor							,       ):
         lowercase     :Any   						=    self.sigmas.to(device=original_samples.device							,       dtype=original_samples.dtype					)
         if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase					):
           # mps does not support float64
           lowercase     :str   						=    self.timesteps.to(original_samples.device							,       dtype=torch.floataa					)
           lowercase     :Union[str, Any]   						=    timesteps.to(original_samples.device							,       dtype=torch.floataa					)
         else:
           lowercase     :Union[str, Any]   						=    self.timesteps.to(original_samples.device					)
           lowercase     :List[str]   						=    timesteps.to(original_samples.device					)

         lowercase     :Optional[int]   						=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

         lowercase     :List[Any]   						=    sigmas[step_indices].flatten()
         while len(sigma.shape					) < len(original_samples.shape					):
           lowercase     :Optional[Any]   						=    sigma.unsqueeze(-1					)

         lowercase     :List[str]   						=    original_samples + noise * sigma
         return noisy_samples






       def __len__( self:       Dict					):
         return self.config.num_train_timesteps




import torch

from accelerate import Accelerator, DistributedType




class 						__snake_case						:





      '''simple docstring'''






      def __enter__(       self  :					Union[str, Any])					->Dict:


          """simple docstring"""


          if self.enabled:
              _lowerCamelCase  :		str			    =    self.model.no_sync()
              self.model_sync_obj.__enter__()

          return self



      def __exit__(       self  :					List[str]     ,	_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					int)					->Optional[Any]:


          """simple docstring"""


          if self.enabled:
              # Average all models on exit
              self._sync_and_avg_model_params()
              self.model_sync_obj.__exit__(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase)



      def __init__(       self  :					Union[str, Any]     ,	_UpperCamelCase  :					Accelerator     ,	_UpperCamelCase  :					torch.nn.Module     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					bool = True)					->int:


          """simple docstring"""


          if accelerator.distributed_type not in [
              DistributedType.NO,
              DistributedType.MULTI_CPU,
              DistributedType.MULTI_GPU,
          ]:
              raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""")
          _lowerCamelCase  :		str			    =    enabled and accelerator.distributed_type != DistributedType.NO
          _lowerCamelCase  :		Optional[int]			    =    0
          if self.enabled:
              _lowerCamelCase  :		Union[str, Any]			    =    accelerator
              _lowerCamelCase  :		int			    =    model
              _lowerCamelCase  :		int			    =    local_sgd_steps



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Union[str, Any]:


          """simple docstring"""


          self.num_steps += 1
          if not self.enabled:
              return

          if self.num_steps % self.local_sgd_steps == 0:
              self._sync_and_avg_model_params()




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->List[str]:


          """simple docstring"""


          self.accelerator.wait_for_everyone()
          with self.accelerator.autocast():
              for param in self.model.parameters():
                  _lowerCamelCase  :		Union[str, Any]			    =    self.accelerator.reduce(param.data     ,	reduction="""mean""")




