


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


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




@dataclass
class  A_ (			lowerCamelCase_  ):







 """simple docstring"""





 _UpperCamelCase					:			Optional[int]     	=       42




class  A_ (			lowerCamelCase_   ,    lowerCamelCase_  ):







 """simple docstring"""





 _UpperCamelCase					:			Optional[Any]     	=       1
 @register_to_config
 def __init__(      self       :			Union[str, Any]							,     _a       :			int = 4_0							,     _a       :			float = 0.0_0_2							,     _a       :			float = 8_0.0							,     _a       :			float = 0.5							,     _a       :			float = 1.0							,     _a       :			float = 7.0							,     _a       :			bool = True							,     )     ->      Tuple:
      UpperCAmelCase    :					Dict			=      sigma_max

      UpperCAmelCase    :					Optional[Any]			=      np.linspace(0							,     1							,     _lowercase					)
      UpperCAmelCase    :					List[Any]			=      self._convert_to_karras(_lowercase					)
      UpperCAmelCase    :					str			=      self.sigma_to_t(_lowercase					)

      # setable values
      UpperCAmelCase    :					List[Any]			=      None
      UpperCAmelCase    :					Tuple			=      torch.from_numpy(_lowercase					)
      UpperCAmelCase    :					List[str]			=      torch.from_numpy(_lowercase					)
      UpperCAmelCase    :					List[str]			=      False
      UpperCAmelCase    :					List[Any]			=      False
 def 	_UpperCamelCase     (      self       :			Union[str, Any]							,     _a       :			Union[str, Any]							,     _a       :			int=None					)     ->      Optional[Any]:






      if schedule_timesteps is None:
           UpperCAmelCase    :					Dict			=      self.timesteps

      UpperCAmelCase    :					Any			=      (schedule_timesteps == timestep).nonzero()
      return indices.item()
 def 	_UpperCamelCase     (      self       :			Optional[Any]							,     _a       :			torch.FloatTensor							,     _a       :			Union[float, torch.FloatTensor]					)     ->      int:






      if isinstance(_lowercase							,     torch.Tensor					):
           UpperCAmelCase    :					Optional[Any]			=      timestep.to(self.timesteps.device					)
      UpperCAmelCase    :					Any			=      self.index_for_timestep(_lowercase					)
      UpperCAmelCase    :					Tuple			=      self.sigmas[step_idx]

      UpperCAmelCase    :					List[Any]			=      sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

      UpperCAmelCase    :					Optional[int]			=      True
      return sample
 def 	_UpperCamelCase     (      self       :			int							,     _a       :			Union[float, np.ndarray]					)     ->      List[Any]:






      if not isinstance(_lowercase							,     np.ndarray					):
           UpperCAmelCase    :					Optional[Any]			=      np.array(_lowercase							,     dtype=np.floataa					)

      UpperCAmelCase    :					List[Any]			=      1_0_0_0 * 0.2_5 * np.log(sigmas + 1E-44					)

      return timesteps
 def 	_UpperCamelCase     (      self       :			Tuple							,     _a       :			Optional[int] = None							,     _a       :			Union[str, torch.device] = None							,     _a       :			Optional[List[int]] = None							,     )     ->      Optional[int]:






      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(_lowercase					)					):
                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}.'					)

           UpperCAmelCase    :					Union[str, Any]			=      np.array(_lowercase							,     dtype=np.intaa					)
           UpperCAmelCase    :					List[Any]			=      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.'					)

           UpperCAmelCase    :					Dict			=      num_inference_steps

           UpperCAmelCase    :					Tuple			=      self.config.num_train_timesteps // self.num_inference_steps
           UpperCAmelCase    :					str			=      (np.arange(0							,     _lowercase					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
           UpperCAmelCase    :					Any			=      False

      # Map timesteps to Karras sigmas directly for multistep sampling
      # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
      UpperCAmelCase    :					List[str]			=      self.config.num_train_timesteps
      UpperCAmelCase    :					int			=      timesteps[::-1].copy()
      UpperCAmelCase    :					str			=      ramp / (num_train_timesteps - 1)
      UpperCAmelCase    :					Union[str, Any]			=      self._convert_to_karras(_lowercase					)
      UpperCAmelCase    :					List[Any]			=      self.sigma_to_t(_lowercase					)

      UpperCAmelCase    :					Dict			=      np.concatenate([sigmas, [self.sigma_min]]					).astype(np.floataa					)
      UpperCAmelCase    :					Optional[Any]			=      torch.from_numpy(_lowercase					).to(device=_lowercase					)

      if str(_lowercase					).startswith("mps"					):
           # mps does not support float64
           UpperCAmelCase    :					Dict			=      torch.from_numpy(_lowercase					).to(_lowercase							,     dtype=torch.floataa					)
      else:
           UpperCAmelCase    :					Dict			=      torch.from_numpy(_lowercase					).to(device=_lowercase					)
 def 	_UpperCamelCase     (      self       :			int							,     _a       :			Any					)     ->      Tuple:
      UpperCAmelCase    :					List[Any]			=      self.config.sigma_min
      UpperCAmelCase    :					int			=      self.config.sigma_max

      UpperCAmelCase    :					Any			=      self.config.rho
      UpperCAmelCase    :					List[Any]			=      sigma_min ** (1 / rho)
      UpperCAmelCase    :					Tuple			=      sigma_max ** (1 / rho)
      UpperCAmelCase    :					Union[str, Any]			=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas
 def 	_UpperCamelCase     (      self       :			Union[str, Any]							,     _a       :			int					)     ->      Tuple:
      UpperCAmelCase    :					Optional[Any]			=      self.config.sigma_data

      UpperCAmelCase    :					Tuple			=      sigma_data**2 / (sigma**2 + sigma_data**2)
      UpperCAmelCase    :					Optional[int]			=      sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
      return c_skip, c_out
 def 	_UpperCamelCase     (      self       :			Optional[Any]							,     _a       :			Optional[Any]					)     ->      Union[str, Any]:
      UpperCAmelCase    :					Dict			=      self.config.sigma_min
      UpperCAmelCase    :					Optional[Any]			=      self.config.sigma_data

      UpperCAmelCase    :					List[str]			=      sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
      UpperCAmelCase    :					int			=      (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
      return c_skip, c_out
 def 	_UpperCamelCase     (      self       :			Optional[Any]							,     _a       :			torch.FloatTensor							,     _a       :			Union[float, torch.FloatTensor]							,     _a       :			torch.FloatTensor							,     _a       :			Optional[torch.Generator] = None							,     _a       :			bool = True							,     )     ->      Optional[int]:






      if (
          isinstance(_lowercase							,     _lowercase					)
          or isinstance(_lowercase							,     torch.IntTensor					)
          or isinstance(_lowercase							,     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(_lowercase							,     torch.Tensor					):
           UpperCAmelCase    :					Any			=      timestep.to(self.timesteps.device					)

      UpperCAmelCase    :					Optional[int]			=      self.config.sigma_min
      UpperCAmelCase    :					List[str]			=      self.config.sigma_max

      UpperCAmelCase    :					str			=      self.index_for_timestep(_lowercase					)

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

      # Get scalings for boundary conditions
      UpperCAmelCase	,			UpperCAmelCase    :					str			=      self.get_scalings_for_boundary_condition(_lowercase					)

      # 1. Denoise model output using boundary conditions
      UpperCAmelCase    :					int			=      c_out * model_output + c_skip * sample
      if self.config.clip_denoised:
           UpperCAmelCase    :					Any			=      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:
           UpperCAmelCase    :					Union[str, Any]			=      randn_tensor(
               model_output.shape							,     dtype=model_output.dtype							,     device=model_output.device							,     generator=_lowercase					)
      else:
           UpperCAmelCase    :					Dict			=      torch.zeros_like(_lowercase					)
      UpperCAmelCase    :					str			=      noise * self.config.s_noise

      UpperCAmelCase    :					Any			=      sigma_next.clamp(min=_lowercase							,     max=_lowercase					)

      # 3. Return noisy sample
      # tau = sigma_hat, eps = sigma_min
      UpperCAmelCase    :					Union[str, Any]			=      denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

      if not return_dict:
           return (prev_sample,)

      return CMStochasticIterativeSchedulerOutput(prev_sample=_lowercase					)
 def 	_UpperCamelCase     (      self       :			List[str]							,     _a       :			torch.FloatTensor							,     _a       :			torch.FloatTensor							,     _a       :			torch.FloatTensor							,     )     ->      List[Any]:
      UpperCAmelCase    :					int			=      self.sigmas.to(device=original_samples.device							,     dtype=original_samples.dtype					)
      if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase					):
           # mps does not support float64
           UpperCAmelCase    :					int			=      self.timesteps.to(original_samples.device							,     dtype=torch.floataa					)
           UpperCAmelCase    :					Dict			=      timesteps.to(original_samples.device							,     dtype=torch.floataa					)
      else:
           UpperCAmelCase    :					List[str]			=      self.timesteps.to(original_samples.device					)
           UpperCAmelCase    :					List[Any]			=      timesteps.to(original_samples.device					)

      UpperCAmelCase    :					Dict			=      [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

      UpperCAmelCase    :					Dict			=      original_samples + noise * sigma
      return noisy_samples

 def __len__(      self       :			Tuple					)     ->      int:
      return self.config.num_train_timesteps




import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


__lowerCamelCase     :					Union[str, Any]				  =						logging.get_logger(__name__)







class __snake_case							(		lowerCamelCase_						):




   def __init__(       self     :							str			,      *_lowercase     :							str			,      **_lowercase     :							str ):



     """simple docstring"""






     warnings.warn(
         """The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use ViTImageProcessor instead."""			,      _lowercase			,      )
     super().__init__(*_lowercase			,      **_lowercase )
