






"""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


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






@dataclass
class 					a						( __snake_case		):
    SCREAMING_SNAKE_CASE						:					torch.FloatTensor






class 					a						( __snake_case  ,				__snake_case		):
    SCREAMING_SNAKE_CASE						:					Union[str, Any]						=			1






    @register_to_config
    def __init__(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							int = 40	,						__SCREAMING_SNAKE_CASE					:							float = 0.002	,						__SCREAMING_SNAKE_CASE					:							float = 80.0	,						__SCREAMING_SNAKE_CASE					:							float = 0.5	,						__SCREAMING_SNAKE_CASE					:							float = 1.0	,						__SCREAMING_SNAKE_CASE					:							float = 7.0	,						__SCREAMING_SNAKE_CASE					:							bool = True	,						)					->		Union[str, Any]:
      # standard deviation of the initial noise distribution
      lowerCamelCase_   					=						sigma_max

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

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






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							str	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]=None )					->		int:
      if schedule_timesteps is None:
        lowerCamelCase_   					=						self.timesteps

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






    def        UpperCamelCase	(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							torch.FloatTensor	,						__SCREAMING_SNAKE_CASE					:							Union[float, torch.FloatTensor] )					->		torch.FloatTensor:
      # Get sigma corresponding to timestep
      if isinstance(__SCREAMING_SNAKE_CASE	,						torch.Tensor ):
        lowerCamelCase_   					=						timestep.to(self.timesteps.device )
      lowerCamelCase_   					=						self.index_for_timestep(__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						self.sigmas[step_idx]

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

      lowerCamelCase_   					=						True
      return sample






    def        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							Union[float, np.ndarray] )					->		List[Any]:
      if not isinstance(__SCREAMING_SNAKE_CASE	,						np.ndarray ):
        lowerCamelCase_   					=						np.array(__SCREAMING_SNAKE_CASE	,						dtype=np.floataa )

      lowerCamelCase_   					=						1000 * 0.25 * np.log(sigmas + 1e-4_4 )

      return timesteps






    def        UpperCamelCase	(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							Optional[int] = None	,						__SCREAMING_SNAKE_CASE					:							Union[str, torch.device] = None	,						__SCREAMING_SNAKE_CASE					:							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(__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}.''' )

        lowerCamelCase_   					=						np.array(__SCREAMING_SNAKE_CASE	,						dtype=np.intaa )
        lowerCamelCase_   					=						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.''' )

        lowerCamelCase_   					=						num_inference_steps

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

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

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

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






    def        UpperCamelCase	(	self					:							Union[str, Any]	,						__SCREAMING_SNAKE_CASE					:							Optional[Any] )					->		Dict:
      lowerCamelCase_   					=						self.config.sigma_min
      lowerCamelCase_   					=						self.config.sigma_max

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






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							str )					->		Union[str, Any]:
      lowerCamelCase_   					=						self.config.sigma_data

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






    def        UpperCamelCase	(	self					:							int	,						__SCREAMING_SNAKE_CASE					:							str )					->		Optional[Any]:
      lowerCamelCase_   					=						self.config.sigma_min
      lowerCamelCase_   					=						self.config.sigma_data

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






    def        UpperCamelCase	(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							torch.FloatTensor	,						__SCREAMING_SNAKE_CASE					:							Union[float, torch.FloatTensor]	,						__SCREAMING_SNAKE_CASE					:							torch.FloatTensor	,						__SCREAMING_SNAKE_CASE					:							Optional[torch.Generator] = None	,						__SCREAMING_SNAKE_CASE					:							bool = True	,						)					->		Union[CMStochasticIterativeSchedulerOutput, Tuple]:

      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 ):
        lowerCamelCase_   					=						timestep.to(self.timesteps.device )

      lowerCamelCase_   					=						self.config.sigma_min
      lowerCamelCase_   					=						self.config.sigma_max

      lowerCamelCase_   					=						self.index_for_timestep(__SCREAMING_SNAKE_CASE )

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

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

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

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

      # 3. Return noisy sample
      # tau = sigma_hat, eps = sigma_min
      lowerCamelCase_   					=						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        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							torch.FloatTensor	,						__SCREAMING_SNAKE_CASE					:							torch.FloatTensor	,						__SCREAMING_SNAKE_CASE					:							torch.FloatTensor	,						)					->		torch.FloatTensor:
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      lowerCamelCase_   					=						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
        lowerCamelCase_   					=						self.timesteps.to(original_samples.device	,						dtype=torch.floataa )
        lowerCamelCase_   					=						timesteps.to(original_samples.device	,						dtype=torch.floataa )
      else:
        lowerCamelCase_   					=						self.timesteps.to(original_samples.device )
        lowerCamelCase_   					=						timesteps.to(original_samples.device )

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

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

      lowerCamelCase_   					=						original_samples + noise * sigma
      return noisy_samples






    def __len__(	self					:							Optional[Any] )					->		int:
      return self.config.num_train_timesteps












"""simple docstring"""





import numpy as np







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		Tuple			)				->    str:
  lowerCamelCase_   					=						n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  lowerCamelCase_   					=						np.floor(_lowerCamelCase			)
  # we add samples according to how much "left over" probability
  # they had, until we arrive at n_samples
  lowerCamelCase_   					=						int(n_draws - floored.sum()			)
  if need_to_add > 0:
    lowerCamelCase_   					=						continuous - floored
    lowerCamelCase_   					=						np.sort(np.unique(_lowerCamelCase			)			)[::-1]
    # add according to remainder, but break ties
    # randomly to avoid biases
    for value in values:
      ((lowerCamelCase_)					,     )   					=						np.where(remainder == value			)
      # if we need_to_add less than what's in inds
      # we draw randomly from them.
      # if we need to add more, we add them all and
      # go to the next value
      lowerCamelCase_   					=						min(len(_lowerCamelCase			)		,    _lowerCamelCase			)
      lowerCamelCase_   					=						rng.choice(_lowerCamelCase		,    size=_lowerCamelCase		,    replace=_lowerCamelCase			)
      floored[inds] += 1
      need_to_add -= add_now
      if need_to_add == 0:
        break
  return floored.astype(np.intaa			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		Optional[int]=10			)				->    Any:
  lowerCamelCase_					,     lowerCamelCase_   					=						np.unique(_lowerCamelCase		,    return_inverse=_lowerCamelCase			)
  lowerCamelCase_   					=						classes.shape[0]
  lowerCamelCase_   					=						np.bincount(_lowerCamelCase			)
  if np.min(_lowerCamelCase			) < 2:
    raise ValueError('Minimum class count error'			)
  if n_train < n_classes:
    raise ValueError(
        'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)			)
  if n_test < n_classes:
    raise ValueError(
        'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)			)
  lowerCamelCase_   					=						np.split(np.argsort(_lowerCamelCase		,    kind='mergesort'			)		,    np.cumsum(_lowerCamelCase			)[:-1]			)
  for _ in range(_lowerCamelCase			):
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
    lowerCamelCase_   					=						class_counts - n_i
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)

    lowerCamelCase_   					=						[]
    lowerCamelCase_   					=						[]

    for i in range(_lowerCamelCase			):
      lowerCamelCase_   					=						rng.permutation(class_counts[i]			)
      lowerCamelCase_   					=						class_indices[i].take(_lowerCamelCase		,    mode='clip'			)
      train.extend(perm_indices_class_i[: n_i[i]]			)
      test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)

    yield train, test




