
"""simple docstring"""




from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available







@dataclass
class 						lowercase(A__					):
     __snake_case:  Union[str, Any]  				=    {
         '''sld_warmup_steps''': 15,
         '''sld_guidance_scale''': 20,
         '''sld_threshold''': 0.0,
         '''sld_momentum_scale''': 0.0,
         '''sld_mom_beta''': 0.0,
     }
     __snake_case:  int  				=    {
         '''sld_warmup_steps''': 10,
         '''sld_guidance_scale''': 1000,
         '''sld_threshold''': 0.01,
         '''sld_momentum_scale''': 0.3,
         '''sld_mom_beta''': 0.4,
     }
     __snake_case:  Any  				=    {
         '''sld_warmup_steps''': 7,
         '''sld_guidance_scale''': 2000,
         '''sld_threshold''': 0.025,
         '''sld_momentum_scale''': 0.5,
         '''sld_mom_beta''': 0.7,
     }
     __snake_case:  int  				=    {
         '''sld_warmup_steps''': 0,
         '''sld_guidance_scale''': 5000,
         '''sld_threshold''': 1.0,
         '''sld_momentum_scale''': 0.5,
         '''sld_mom_beta''': 0.7,
     }







@dataclass
class 						lowercase(A__					):
     __snake_case:  Union[List[PIL.Image.Image], np.ndarray]
     __snake_case:  Optional[List[bool]]
     __snake_case:  Optional[Union[List[PIL.Image.Image], np.ndarray]]
     __snake_case:  Optional[str]


if is_transformers_available() and is_torch_available():
    from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
    from .safety_checker import SafeStableDiffusionSafetyChecker





"""simple docstring"""




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 SchedulerMixin


a		:					List[Any]    	=					logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 						lowercase(_lowercase					):
     __snake_case:  torch.FloatTensor







class 						lowercase(_lowercase						,	_lowercase					):
     __snake_case:  List[Any]  				=    1



     @register_to_config
     def __init__(       self    ,							__SCREAMING_SNAKE_CASE = 4_0    ,							__SCREAMING_SNAKE_CASE = 0.0_02    ,							__SCREAMING_SNAKE_CASE = 80.0    ,							__SCREAMING_SNAKE_CASE = 0.5    ,							__SCREAMING_SNAKE_CASE = 1.0    ,							__SCREAMING_SNAKE_CASE = 7.0    ,							__SCREAMING_SNAKE_CASE = True    ,							)		->    Dict:







         """simple docstring"""



         a__    = sigma_max

         a__    = np.linspace(0    ,							1    ,							__SCREAMING_SNAKE_CASE      )
         a__    = self._convert_to_karras(__SCREAMING_SNAKE_CASE      )
         a__    = self.sigma_to_t(__SCREAMING_SNAKE_CASE      )

         # setable values
         a__    = None
         a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      )
         a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      )
         a__    = False
         a__    = False



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=None      )		->    Union[str, Any]:







         """simple docstring"""



         if schedule_timesteps is None:
             a__    = self.timesteps

         a__    = (schedule_timesteps == timestep).nonzero()
         return indices.item()



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    torch.FloatTensor:







         """simple docstring"""



         if isinstance(__SCREAMING_SNAKE_CASE    ,							torch.Tensor      ):
             a__    = timestep.to(self.timesteps.device      )
         a__    = self.index_for_timestep(__SCREAMING_SNAKE_CASE      )
         a__    = self.sigmas[step_idx]

         a__    = sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

         a__    = True
         return sample



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    int:







         """simple docstring"""



         if not isinstance(__SCREAMING_SNAKE_CASE    ,							np.ndarray      ):
             a__    = np.array(__SCREAMING_SNAKE_CASE    ,							dtype=np.floataa      )

         a__    = 1_0_0_0 * 0.25 * np.log(sigmas + 1e-44      )

         return timesteps



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							)		->    Optional[int]:







         """simple docstring"""



         if num_inference_steps is None and timesteps is None:
             raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'      )

         if num_inference_steps is not None and timesteps is not None:
             raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.'      )

         # Follow DDPMScheduler custom timesteps logic
         if timesteps is not None:
             for i in range(1    ,							len(__SCREAMING_SNAKE_CASE      )      ):
                 if timesteps[i] >= timesteps[i - 1]:
                     raise ValueError('`timesteps` must be in descending order.'      )

             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
                     f'`timesteps` must start before `self.config.train_timesteps`:'
                     f' {self.config.num_train_timesteps}.'      )

             a__    = np.array(__SCREAMING_SNAKE_CASE    ,							dtype=np.intaa      )
             a__    = True
         else:
             if num_inference_steps > self.config.num_train_timesteps:
                 raise ValueError(
                     f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                     f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                     f' maximal {self.config.num_train_timesteps} timesteps.'      )

             a__    = num_inference_steps

             a__    = self.config.num_train_timesteps // self.num_inference_steps
             a__    = (np.arange(0    ,							__SCREAMING_SNAKE_CASE      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
             a__    = False

         # Map timesteps to Karras sigmas directly for multistep sampling
         # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
         a__    = self.config.num_train_timesteps
         a__    = timesteps[::-1].copy()
         a__    = ramp / (num_train_timesteps - 1)
         a__    = self._convert_to_karras(__SCREAMING_SNAKE_CASE      )
         a__    = self.sigma_to_t(__SCREAMING_SNAKE_CASE      )

         a__    = np.concatenate([sigmas, [self.sigma_min]]      ).astype(np.floataa      )
         a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      ).to(device=__SCREAMING_SNAKE_CASE      )

         if str(__SCREAMING_SNAKE_CASE      ).startswith('mps'      ):
             # mps does not support float64
             a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      ).to(__SCREAMING_SNAKE_CASE    ,							dtype=torch.floataa      )
         else:
             a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      ).to(device=__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         a__    = self.config.sigma_min
         a__    = self.config.sigma_max

         a__    = self.config.rho
         a__    = sigma_min ** (1 / rho)
         a__    = sigma_max ** (1 / rho)
         a__    = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas



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







         """simple docstring"""



         a__    = self.config.sigma_data

         a__    = sigma_data**2 / (sigma**2 + sigma_data**2)
         a__    = sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    List[str]:







         """simple docstring"""



         a__    = self.config.sigma_min
         a__    = self.config.sigma_data

         a__    = sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
         a__    = (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = True    ,							)		->    Union[CMStochasticIterativeSchedulerOutput, Tuple]:







         """simple docstring"""



         if (
             isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
             or isinstance(__SCREAMING_SNAKE_CASE    ,							torch.IntTensor      )
             or isinstance(__SCREAMING_SNAKE_CASE    ,							torch.LongTensor      )
         ):
             raise ValueError(
                 (
                     'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                     f' `{self.__class__}.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(__SCREAMING_SNAKE_CASE    ,							torch.Tensor      ):
             a__    = timestep.to(self.timesteps.device      )

         a__    = self.config.sigma_min
         a__    = self.config.sigma_max

         a__    = self.index_for_timestep(__SCREAMING_SNAKE_CASE      )

         # sigma_next corresponds to next_t in original implementation
         a__    = self.sigmas[step_index]
         if step_index + 1 < self.config.num_train_timesteps:
             a__    = self.sigmas[step_index + 1]
         else:
             # Set sigma_next to sigma_min
             a__    = self.sigmas[-1]

         # Get scalings for boundary conditions
         a__  ,    a__    = self.get_scalings_for_boundary_condition(__SCREAMING_SNAKE_CASE      )

         # 1. Denoise model output using boundary conditions
         a__    = c_out * model_output + c_skip * sample
         if self.config.clip_denoised:
             a__    = denoised.clamp(-1    ,							1      )

         # 2. Sample z ~ N(0, s_noise^2 * I)
         # Noise is not used for onestep sampling.
         if len(self.timesteps      ) > 1:
             a__    = randn_tensor(
                 model_output.shape    ,							dtype=model_output.dtype    ,							device=model_output.device    ,							generator=__SCREAMING_SNAKE_CASE      )
         else:
             a__    = torch.zeros_like(__SCREAMING_SNAKE_CASE      )
         a__    = noise * self.config.s_noise

         a__    = sigma_next.clamp(min=__SCREAMING_SNAKE_CASE    ,							max=__SCREAMING_SNAKE_CASE      )

         # 3. Return noisy sample
         # tau = sigma_hat, eps = sigma_min
         a__    = denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

         if not return_dict:
             return (prev_sample,)

         return CMStochasticIterativeSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							)		->    torch.FloatTensor:







         """simple docstring"""



         a__    = self.sigmas.to(device=original_samples.device    ,							dtype=original_samples.dtype      )
         if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE      ):
             # mps does not support float64
             a__    = self.timesteps.to(original_samples.device    ,							dtype=torch.floataa      )
             a__    = timesteps.to(original_samples.device    ,							dtype=torch.floataa      )
         else:
             a__    = self.timesteps.to(original_samples.device      )
             a__    = timesteps.to(original_samples.device      )

         a__    = [(schedule_timesteps == t).nonzero().item() for t in timesteps]

         a__    = sigmas[step_indices].flatten()
         while len(sigma.shape      ) < len(original_samples.shape      ):
             a__    = sigma.unsqueeze(-1      )

         a__    = original_samples + noise * sigma
         return noisy_samples




     def __len__(       self      )		->    str:







         """simple docstring"""



         return self.config.num_train_timesteps


