






'''simple docstring'''

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 				snake_case       (  a_				:			Dict     , a_				:			Optional[Any]=0.999     , a_				:			List[str]="cosine"     , )							->					Optional[Any]:





      """simple docstring"""

      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

      UpperCamelCase_					: str            =				[]
      for i in range(a_						):
            UpperCamelCase_					: Optional[int]            =				i / num_diffusion_timesteps
            UpperCamelCase_					: str            =				(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      A     (   SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Optional[Any]			  =    [e.name for e in KarrasDiffusionSchedulers]
 __a      :    Any			  =    2
 @register_to_config
 def __init__(				self      ,  __lowerCAmelCase = 10_00      ,  __lowerCAmelCase = 0.0_00_85      ,  __lowerCAmelCase = 0.0_12      ,  __lowerCAmelCase = "linear"      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = "epsilon"      ,  __lowerCAmelCase = False      ,  __lowerCAmelCase = False      ,  __lowerCAmelCase = 1.0      ,  __lowerCAmelCase = "linspace"      ,  __lowerCAmelCase = 0      ,  ):
       if trained_betas is not None:
             UpperCamelCase_					: List[str]            =				torch.tensor(__lowerCAmelCase      ,  dtype=torch.floataa			)
       elif beta_schedule == "linear":
             UpperCamelCase_					: int            =				torch.linspace(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  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      ,  __lowerCAmelCase      ,  dtype=torch.floataa			) ** 2
             )
       elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             UpperCamelCase_					: Any            =				betas_for_alpha_bar(__lowerCAmelCase      ,  alpha_transform_type="""cosine"""			)
       elif beta_schedule == "exp":
             UpperCamelCase_					: str            =				betas_for_alpha_bar(__lowerCAmelCase      ,  alpha_transform_type="""exp"""			)
       else:
             raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"			)

       UpperCamelCase_					: Tuple            =				1.0 - self.betas
       UpperCamelCase_					: Union[str, Any]            =				torch.cumprod(self.alphas      ,  dim=0			)

       #  set all values
       self.set_timesteps(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)
       UpperCamelCase_					: Optional[int]            =				use_karras_sigmas
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=None			):
       if schedule_timesteps is None:
             UpperCamelCase_					: str            =				self.timesteps

       UpperCamelCase_					: Any            =				(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[Any]            =				1 if len(__lowerCAmelCase			) > 1 else 0
       else:
             UpperCamelCase_					: int            =				timestep.cpu().item() if torch.is_tensor(__lowerCAmelCase			) else timestep
             UpperCamelCase_					: List[Any]            =				self._index_counter[timestep_int]

       return indices[pos].item()
 @property
 def 	_UpperCAmelCase							(				self			):
       # 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      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  ):
       UpperCamelCase_					: List[Any]            =				self.index_for_timestep(__lowerCAmelCase			)

       UpperCamelCase_					: Any            =				self.sigmas[step_index]
       UpperCamelCase_					: List[str]            =				sample / ((sigma**2 + 1) ** 0.5)
       return sample
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  ):
       UpperCamelCase_					: Optional[int]            =				num_inference_steps

       UpperCamelCase_					: int            =				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_					: List[str]            =				np.linspace(0      ,  num_train_timesteps - 1      ,  __lowerCAmelCase      ,  dtype=__lowerCAmelCase			)[::-1].copy()
       elif self.config.timestep_spacing == "leading":
             UpperCamelCase_					: Tuple            =				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_					: Union[str, Any]            =				(np.arange(0      ,  __lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(__lowerCAmelCase			)
             timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
             UpperCamelCase_					: Optional[int]            =				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_					: Dict            =				(np.arange(__lowerCAmelCase      ,  0      ,  -step_ratio			)).round().copy().astype(__lowerCAmelCase			)
             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_					: Any            =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
       UpperCamelCase_					: Tuple            =				np.log(__lowerCAmelCase			)
       UpperCamelCase_					: Any            =				np.interp(__lowerCAmelCase      ,  np.arange(0      ,  len(__lowerCAmelCase			)			)      ,  __lowerCAmelCase			)

       if self.config.use_karras_sigmas:
             UpperCamelCase_					: Optional[Any]            =				self._convert_to_karras(in_sigmas=__lowerCAmelCase      ,  num_inference_steps=self.num_inference_steps			)
             UpperCamelCase_					: List[Any]            =				np.array([self._sigma_to_t(__lowerCAmelCase      ,  __lowerCAmelCase			) for sigma in sigmas]			)

       UpperCamelCase_					: int            =				np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
       UpperCamelCase_					: List[str]            =				torch.from_numpy(__lowerCAmelCase			).to(device=__lowerCAmelCase			)
       UpperCamelCase_					: Dict            =				torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2			), sigmas[-1:]]			)

       UpperCamelCase_					: List[Any]            =				torch.from_numpy(__lowerCAmelCase			)
       UpperCamelCase_					: str            =				torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2			)]			)

       if str(__lowerCAmelCase			).startswith("""mps"""			):
             # mps does not support float64
             UpperCamelCase_					: int            =				timesteps.to(__lowerCAmelCase      ,  dtype=torch.floataa			)
       else:
             UpperCamelCase_					: List[str]            =				timesteps.to(device=__lowerCAmelCase			)

       # empty dt and derivative
       UpperCamelCase_					: Optional[int]            =				None
       UpperCamelCase_					: int            =				None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       UpperCamelCase_					: Optional[Any]            =				defaultdict(__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       # get log sigma
       UpperCamelCase_					: Optional[Any]            =				np.log(__lowerCAmelCase			)

       # get distribution
       UpperCamelCase_					: Tuple            =				log_sigma - log_sigmas[:, np.newaxis]

       # get sigmas range
       UpperCamelCase_					: Optional[Any]            =				np.cumsum((dists >= 0)      ,  axis=0			).argmax(axis=0			).clip(max=log_sigmas.shape[0] - 2			)
       UpperCamelCase_					: Tuple            =				low_idx + 1

       UpperCamelCase_					: int            =				log_sigmas[low_idx]
       UpperCamelCase_					: Dict            =				log_sigmas[high_idx]

       # interpolate sigmas
       UpperCamelCase_					: Optional[int]            =				(low - log_sigma) / (low - high)
       UpperCamelCase_					: Dict            =				np.clip(__lowerCAmelCase      ,  0      ,  1			)

       # transform interpolation to time range
       UpperCamelCase_					: Any            =				(1 - w) * low_idx + w * high_idx
       UpperCamelCase_					: Any            =				t.reshape(sigma.shape			)
       return t
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: float            =				in_sigmas[-1].item()
       UpperCamelCase_					: float            =				in_sigmas[0].item()

       UpperCamelCase_					: Dict            =				7.0  # 7.0 is the value used in the paper
       UpperCamelCase_					: Any            =				np.linspace(0      ,  1      ,  __lowerCAmelCase			)
       UpperCamelCase_					: Dict            =				sigma_min ** (1 / rho)
       UpperCamelCase_					: List[Any]            =				sigma_max ** (1 / rho)
       UpperCamelCase_					: Any            =				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas
 @property
 def 	_UpperCAmelCase							(				self			):
       return self.dt is None
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase = True      ,  ):
       UpperCamelCase_					: Optional[Any]            =				self.index_for_timestep(__lowerCAmelCase			)

       # advance index counter by 1
       UpperCamelCase_					: List[Any]            =				timestep.cpu().item() if torch.is_tensor(__lowerCAmelCase			) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
             UpperCamelCase_					: Tuple            =				self.sigmas[step_index]
             UpperCamelCase_					: Union[str, Any]            =				self.sigmas[step_index + 1]
       else:
             # 2nd order / Heun's method
             UpperCamelCase_					: str            =				self.sigmas[step_index - 1]
             UpperCamelCase_					: Tuple            =				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[str]            =				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_					: List[Any]            =				sigma_hat if self.state_in_first_order else sigma_next
             UpperCamelCase_					: Tuple            =				sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
             UpperCamelCase_					: Any            =				sigma_hat if self.state_in_first_order else sigma_next
             UpperCamelCase_					: Tuple            =				model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
       elif self.config.prediction_type == "sample":
             UpperCamelCase_					: List[str]            =				model_output
       else:
             raise ValueError(
                 F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"			)

       if self.config.clip_sample:
             UpperCamelCase_					: Optional[int]            =				pred_original_sample.clamp(
                 -self.config.clip_sample_range      ,  self.config.clip_sample_range			)

       if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             UpperCamelCase_					: Dict            =				(sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             UpperCamelCase_					: str            =				sigma_next - sigma_hat

             # store for 2nd order step
             UpperCamelCase_					: Any            =				derivative
             UpperCamelCase_					: Union[str, Any]            =				dt
             UpperCamelCase_					: str            =				sample
       else:
             # 2. 2nd order / Heun's method
             UpperCamelCase_					: Dict            =				(sample - pred_original_sample) / sigma_next
             UpperCamelCase_					: int            =				(self.prev_derivative + derivative) / 2

             # 3. take prev timestep & sample
             UpperCamelCase_					: Optional[int]            =				self.dt
             UpperCamelCase_					: int            =				self.sample

             # free dt and derivative
             # Note, this puts the scheduler in "first order mode"
             UpperCamelCase_					: Tuple            =				None
             UpperCamelCase_					: Optional[Any]            =				None
             UpperCamelCase_					: Any            =				None

       UpperCamelCase_					: List[str]            =				sample + derivative * dt

       if not return_dict:
             return (prev_sample,)

       return SchedulerOutput(prev_sample=__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  ):
       # 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(__lowerCAmelCase			):
             # mps does not support float64
             UpperCamelCase_					: Dict            =				self.timesteps.to(original_samples.device      ,  dtype=torch.floataa			)
             UpperCamelCase_					: Optional[Any]            =				timesteps.to(original_samples.device      ,  dtype=torch.floataa			)
       else:
             UpperCamelCase_					: Dict            =				self.timesteps.to(original_samples.device			)
             UpperCamelCase_					: Union[str, Any]            =				timesteps.to(original_samples.device			)

       UpperCamelCase_					: Union[str, Any]            =				[self.index_for_timestep(__lowerCAmelCase      ,  __lowerCAmelCase			) for t in timesteps]

       UpperCamelCase_					: Tuple            =				sigmas[step_indices].flatten()
       while len(sigma.shape			) < len(original_samples.shape			):
             UpperCamelCase_					: Union[str, Any]            =				sigma.unsqueeze(-1			)

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





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














'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
      from .multicontrolnet import MultiControlNetModel
      from .pipeline_controlnet import StableDiffusionControlNetPipeline
      from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
      from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
      from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline






