

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




@dataclass
class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   torch.FloatTensor







class    _lowerCAmelCase    (      lowercase	,lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   str  						=      1


 @register_to_config
 def __init__(     self				:     List[str],   UpperCAmelCase__				:     int = 4_0,   UpperCAmelCase__				:     float = 0.002,   UpperCAmelCase__				:     float = 80.0,   UpperCAmelCase__				:     float = 0.5,   UpperCAmelCase__				:     float = 1.0,   UpperCAmelCase__				:     float = 7.0,   UpperCAmelCase__				:     bool = True,   ):
    # standard deviation of the initial noise distribution
    __lowercase								=		sigma_max

    __lowercase								=		np.linspace(0,   1,   UpperCAmelCase__      )
    __lowercase								=		self._convert_to_karras(UpperCAmelCase__      )
    __lowercase								=		self.sigma_to_t(UpperCAmelCase__      )

    # setable values
    __lowercase								=		None
    __lowercase								=		torch.from_numpy(UpperCAmelCase__      )
    __lowercase								=		torch.from_numpy(UpperCAmelCase__      )
    __lowercase								=		False
    __lowercase								=		False


 def    _lowercase					(     self				:     List[Any],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     List[str]=None      ):
    if schedule_timesteps is None:
       __lowercase								=		self.timesteps

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


 def    _lowercase					(     self				:     Optional[Any],   UpperCAmelCase__				:     torch.FloatTensor,   UpperCAmelCase__				:     Union[float, torch.FloatTensor]      ):
    # Get sigma corresponding to timestep
    if isinstance(UpperCAmelCase__,   torch.Tensor      ):
       __lowercase								=		timestep.to(self.timesteps.device      )
    __lowercase								=		self.index_for_timestep(UpperCAmelCase__      )
    __lowercase								=		self.sigmas[step_idx]

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

    __lowercase								=		True
    return sample


 def    _lowercase					(     self				:     List[str],   UpperCAmelCase__				:     Union[float, np.ndarray]      ):
    if not isinstance(UpperCAmelCase__,   np.ndarray      ):
       __lowercase								=		np.array(UpperCAmelCase__,   dtype=np.floataa      )

    __lowercase								=		1_0_0_0 * 0.25 * np.log(sigmas + 1E-44      )

    return timesteps


 def    _lowercase					(     self				:     List[str],   UpperCAmelCase__				:     Optional[int] = None,   UpperCAmelCase__				:     Union[str, torch.device] = None,   UpperCAmelCase__				:     Optional[List[int]] = None,   ):
    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(UpperCAmelCase__      )      ):
          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}."""      )

       __lowercase								=		np.array(UpperCAmelCase__,   dtype=np.intaa      )
       __lowercase								=		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."""      )

       __lowercase								=		num_inference_steps

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

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

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

    if str(UpperCAmelCase__      ).startswith("mps"      ):
       # mps does not support float64
       __lowercase								=		torch.from_numpy(UpperCAmelCase__      ).to(UpperCAmelCase__,   dtype=torch.floataa      )
    else:
       __lowercase								=		torch.from_numpy(UpperCAmelCase__      ).to(device=UpperCAmelCase__      )


 def    _lowercase					(     self				:     Any,   UpperCAmelCase__				:     List[str]      ):
    __lowercase								=		self.config.sigma_min
    __lowercase								=		self.config.sigma_max

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


 def    _lowercase					(     self				:     Dict,   UpperCAmelCase__				:     Union[str, Any]      ):
    __lowercase								=		self.config.sigma_data

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


 def    _lowercase					(     self				:     List[str],   UpperCAmelCase__				:     Dict      ):
    __lowercase								=		self.config.sigma_min
    __lowercase								=		self.config.sigma_data

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


 def    _lowercase					(     self				:     Union[str, Any],   UpperCAmelCase__				:     torch.FloatTensor,   UpperCAmelCase__				:     Union[float, torch.FloatTensor],   UpperCAmelCase__				:     torch.FloatTensor,   UpperCAmelCase__				:     Optional[torch.Generator] = None,   UpperCAmelCase__				:     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"
               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(UpperCAmelCase__,   torch.Tensor      ):
       __lowercase								=		timestep.to(self.timesteps.device      )

    __lowercase								=		self.config.sigma_min
    __lowercase								=		self.config.sigma_max

    __lowercase								=		self.index_for_timestep(UpperCAmelCase__      )

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

    # Get scalings for boundary conditions
    __lowercase						,__lowercase								=		self.get_scalings_for_boundary_condition(UpperCAmelCase__      )

    # 1. Denoise model output using boundary conditions
    __lowercase								=		c_out * model_output + c_skip * sample
    if self.config.clip_denoised:
       __lowercase								=		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:
       __lowercase								=		randn_tensor(
           model_output.shape,   dtype=model_output.dtype,   device=model_output.device,   generator=UpperCAmelCase__      )
    else:
       __lowercase								=		torch.zeros_like(UpperCAmelCase__      )
    __lowercase								=		noise * self.config.s_noise

    __lowercase								=		sigma_next.clamp(min=UpperCAmelCase__,   max=UpperCAmelCase__      )

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

    if not return_dict:
       return (prev_sample,)

    return CMStochasticIterativeSchedulerOutput(prev_sample=UpperCAmelCase__      )


 def    _lowercase					(     self				:     Any,   UpperCAmelCase__				:     torch.FloatTensor,   UpperCAmelCase__				:     torch.FloatTensor,   UpperCAmelCase__				:     torch.FloatTensor,   ):
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    __lowercase								=		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								=		self.timesteps.to(original_samples.device,   dtype=torch.floataa      )
       __lowercase								=		timesteps.to(original_samples.device,   dtype=torch.floataa      )
    else:
       __lowercase								=		self.timesteps.to(original_samples.device      )
       __lowercase								=		timesteps.to(original_samples.device      )

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

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

    __lowercase								=		original_samples + noise * sigma
    return noisy_samples



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







"""simple docstring"""





def     _A    (    UpperCamelCase_	:			str)      ->		int:
   '''simple docstring'''


   __lowercase								=		0
   for plain_chr in plain_text:
      __lowercase								=		(
          ord(UpperCamelCase_) + (hash_value << 6) + (hash_value << 16) - hash_value
      )
   return hash_value




