





# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)








import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			List[Any]=0.999		,   __UpperCAmelCase:			Tuple="cosine"		,   )      ->	Optional[Any]:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__UpperCAmelCase:			List[Any]      ):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__UpperCAmelCase:			List[Any]      ):
            return math.exp(t * -12.0      )

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

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






class     lowercase__       (	__lowerCamelCase			,				__lowerCamelCase				):

   '''simple docstring'''


   a						:							Optional[Any]   		=		[e.name for e in KarrasDiffusionSchedulers]
   a						:							Union[str, Any]   		=		2




   @register_to_config
   def __init__(    self,		__magic_name__ = 1000,		__magic_name__ = 0.0_0085,		__magic_name__ = 0.012,		__magic_name__ = "linear",		__magic_name__ = None,		__magic_name__ = "epsilon",		__magic_name__ = "linspace",		__magic_name__ = 0,		) ->      Tuple:






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

       UpperCamelCase__							:				Optional[int]          =      1.0 - self.betas
       UpperCamelCase__							:				List[Any]          =      torch.cumprod(self.alphas,		dim=0							)

       #  set all values
       self.set_timesteps(__magic_name__,		__magic_name__,		__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=None							) ->      str:






       """simple docstring"""
       if schedule_timesteps is None:
           UpperCamelCase__							:				Dict          =      self.timesteps

       UpperCamelCase__							:				Tuple          =      (schedule_timesteps == timestep).nonzero()

       # The sigma index that is taken for the **very** first `step`
       # is always the second index (or the last index if there is only 1)
       # This way we can ensure we don't accidentally skip a sigma in
       # case we start in the middle of the denoising schedule (e.g. for image-to-image)
       if len(self._index_counter							) == 0:
           UpperCamelCase__							:				List[str]          =      1 if len(__magic_name__							) > 1 else 0
       else:
           UpperCamelCase__							:				List[Any]          =      timestep.cpu().item() if torch.is_tensor(__magic_name__							) else timestep
           UpperCamelCase__							:				int          =      self._index_counter[timestep_int]

       return indices[pos].item()




   @property
   def       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       # standard deviation of the initial noise distribution
       if self.config.timestep_spacing in ["linspace", "trailing"]:
           return self.sigmas.max()

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




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		) ->      torch.FloatTensor:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      self.index_for_timestep(__magic_name__							)

       if self.state_in_first_order:
           UpperCamelCase__							:				str          =      self.sigmas[step_index]
       else:
           UpperCamelCase__							:				Optional[int]          =      self.sigmas_interpol[step_index]

       UpperCamelCase__							:				Optional[int]          =      sample / ((sigma**2 + 1) ** 0.5)
       return sample




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      num_inference_steps

       UpperCamelCase__							:				Tuple          =      num_train_timesteps or self.config.num_train_timesteps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
           UpperCamelCase__							:				Union[str, Any]          =      np.linspace(0,		num_train_timesteps - 1,		__magic_name__,		dtype=__magic_name__							)[::-1].copy()
       elif self.config.timestep_spacing == "leading":
           UpperCamelCase__							:				Union[str, Any]          =      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
           UpperCamelCase__							:				List[str]          =      (np.arange(0,		__magic_name__							) * step_ratio).round()[::-1].copy().astype(__magic_name__							)
           timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
           UpperCamelCase__							:				Optional[Any]          =      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
           UpperCamelCase__							:				List[str]          =      (np.arange(__magic_name__,		0,		-step_ratio							)).round().copy().astype(__magic_name__							)
           timesteps -= 1
       else:
           raise ValueError(
               f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."							)

       UpperCamelCase__							:				int          =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
       UpperCamelCase__							:				Optional[Any]          =      torch.from_numpy(np.log(__magic_name__							)							).to(__magic_name__							)

       UpperCamelCase__							:				Any          =      np.interp(__magic_name__,		np.arange(0,		len(__magic_name__							)							),		__magic_name__							)
       UpperCamelCase__							:				Union[str, Any]          =      np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
       UpperCamelCase__							:				Any          =      torch.from_numpy(__magic_name__							).to(device=__magic_name__							)

       # interpolate sigmas
       UpperCamelCase__							:				int          =      sigmas.log().lerp(sigmas.roll(1							).log(),		0.5							).exp()

       UpperCamelCase__							:				List[str]          =      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2							), sigmas[-1:]]							)
       UpperCamelCase__							:				str          =      torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2							), sigmas_interpol[-1:]]							)

       if str(__magic_name__							).startswith('''mps'''							):
           # mps does not support float64
           UpperCamelCase__							:				Optional[Any]          =      torch.from_numpy(__magic_name__							).to(__magic_name__,		dtype=torch.floataa							)
       else:
           UpperCamelCase__							:				List[Any]          =      torch.from_numpy(__magic_name__							).to(__magic_name__							)

       # interpolate timesteps
       UpperCamelCase__							:				str          =      self.sigma_to_t(__magic_name__							).to(__magic_name__,		dtype=timesteps.dtype							)
       UpperCamelCase__							:				Dict          =      torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]),		dim=-1							).flatten()

       UpperCamelCase__							:				Optional[int]          =      torch.cat([timesteps[:1], interleaved_timesteps]							)

       UpperCamelCase__							:				List[str]          =      None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       UpperCamelCase__							:				Dict          =      defaultdict(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Optional[Any]:






       """simple docstring"""
       # get log sigma
       UpperCamelCase__							:				Any          =      sigma.log()

       # get distribution
       UpperCamelCase__							:				List[str]          =      log_sigma - self.log_sigmas[:, None]

       # get sigmas range
       UpperCamelCase__							:				int          =      dists.ge(0							).cumsum(dim=0							).argmax(dim=0							).clamp(max=self.log_sigmas.shape[0] - 2							)
       UpperCamelCase__							:				Optional[Any]          =      low_idx + 1

       UpperCamelCase__							:				str          =      self.log_sigmas[low_idx]
       UpperCamelCase__							:				int          =      self.log_sigmas[high_idx]

       # interpolate sigmas
       UpperCamelCase__							:				List[Any]          =      (low - log_sigma) / (low - high)
       UpperCamelCase__							:				str          =      w.clamp(0,		1							)

       # transform interpolation to time range
       UpperCamelCase__							:				Tuple          =      (1 - w) * low_idx + w * high_idx
       UpperCamelCase__							:				int          =      t.view(sigma.shape							)
       return t




   @property
   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






       """simple docstring"""
       return self.sample is None




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = True,		) ->      Union[SchedulerOutput, Tuple]:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self.index_for_timestep(__magic_name__							)

       # advance index counter by 1
       UpperCamelCase__							:				int          =      timestep.cpu().item() if torch.is_tensor(__magic_name__							) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
           UpperCamelCase__							:				Optional[Any]          =      self.sigmas[step_index]
           UpperCamelCase__							:				Union[str, Any]          =      self.sigmas_interpol[step_index + 1]
           UpperCamelCase__							:				List[Any]          =      self.sigmas[step_index + 1]
       else:
           # 2nd order / KDPM2's method
           UpperCamelCase__							:				Tuple          =      self.sigmas[step_index - 1]
           UpperCamelCase__							:				Tuple          =      self.sigmas_interpol[step_index]
           UpperCamelCase__							:				Dict          =      self.sigmas[step_index]

       # currently only gamma=0 is supported. This usually works best anyways.
       # We can support gamma in the future but then need to scale the timestep before
       # passing it to the model which requires a change in API
       UpperCamelCase__							:				Optional[int]          =      0
       UpperCamelCase__							:				List[Any]          =      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
           UpperCamelCase__							:				Union[str, Any]          =      sigma_hat if self.state_in_first_order else sigma_interpol
           UpperCamelCase__							:				List[str]          =      sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
           UpperCamelCase__							:				List[Any]          =      sigma_hat if self.state_in_first_order else sigma_interpol
           UpperCamelCase__							:				Optional[Any]          =      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
               sample / (sigma_input**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`"							)

       if self.state_in_first_order:
           # 2. Convert to an ODE derivative for 1st order
           UpperCamelCase__							:				List[Any]          =      (sample - pred_original_sample) / sigma_hat
           # 3. delta timestep
           UpperCamelCase__							:				List[str]          =      sigma_interpol - sigma_hat

           # store for 2nd order step
           UpperCamelCase__							:				Dict          =      sample
       else:
           # DPM-Solver-2
           # 2. Convert to an ODE derivative for 2nd order
           UpperCamelCase__							:				List[str]          =      (sample - pred_original_sample) / sigma_interpol

           # 3. delta timestep
           UpperCamelCase__							:				Union[str, Any]          =      sigma_next - sigma_hat

           UpperCamelCase__							:				Union[str, Any]          =      self.sample
           UpperCamelCase__							:				Dict          =      None

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

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		) ->      torch.FloatTensor:






       """simple docstring"""
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCamelCase__							:				List[str]          =      self.sigmas.to(device=original_samples.device,		dtype=original_samples.dtype							)
       if original_samples.device.type == "mps" and torch.is_floating_point(__magic_name__							):
           # mps does not support float64
           UpperCamelCase__							:				Union[str, Any]          =      self.timesteps.to(original_samples.device,		dtype=torch.floataa							)
           UpperCamelCase__							:				Tuple          =      timesteps.to(original_samples.device,		dtype=torch.floataa							)
       else:
           UpperCamelCase__							:				str          =      self.timesteps.to(original_samples.device							)
           UpperCamelCase__							:				int          =      timesteps.to(original_samples.device							)

       UpperCamelCase__							:				Any          =      [self.index_for_timestep(__magic_name__,		__magic_name__							) for t in timesteps]

       UpperCamelCase__							:				List[str]          =      sigmas[step_indices].flatten()
       while len(sigma.shape							) < len(original_samples.shape							):
           UpperCamelCase__							:				int          =      sigma.unsqueeze(-1							)

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




   def __len__(    self							) ->      Any:






       """simple docstring"""
       return self.config.num_train_timesteps

