



'''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


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






@dataclass
class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  a_							 =42






class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  a_							 =1




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

    __lowerCamelCase	:	str   	=			np.linspace(0      ,     1      ,     _a       )
    __lowerCamelCase	:	List[str]   	=			self._convert_to_karras(_a       )
    __lowerCamelCase	:	int   	=			self.sigma_to_t(_a       )

    # setable values
    __lowerCamelCase	:	Optional[int]   	=			None
    __lowerCamelCase	:	Union[str, Any]   	=			torch.from_numpy(_a       )
    __lowerCamelCase	:	Tuple   	=			torch.from_numpy(_a       )
    __lowerCamelCase	:	Optional[int]   	=			False
    __lowerCamelCase	:	Any   	=			False




  def 						_lowercase					(			self				:     List[Any]      ,     _a				:     Tuple      ,     _a				:     Any=None       ) -> Dict:
    if schedule_timesteps is None:
      __lowerCamelCase	:	Union[str, Any]   	=			self.timesteps

    __lowerCamelCase	:	Any   	=			(schedule_timesteps == timestep).nonzero()
    return indices.item()




  def 						_lowercase					(			self				:     List[str]      ,     _a				:     torch.FloatTensor      ,     _a				:     Union[float, torch.FloatTensor]       ) -> torch.FloatTensor:
    # Get sigma corresponding to timestep
    if isinstance(_a      ,     torch.Tensor       ):
      __lowerCamelCase	:	Any   	=			timestep.to(self.timesteps.device       )
    __lowerCamelCase	:	Optional[Any]   	=			self.index_for_timestep(_a       )
    __lowerCamelCase	:	int   	=			self.sigmas[step_idx]

    __lowerCamelCase	:	str   	=			sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

    __lowerCamelCase	:	Any   	=			True
    return sample




  def 						_lowercase					(			self				:     List[str]      ,     _a				:     Union[float, np.ndarray]       ) -> Optional[int]:
    if not isinstance(_a      ,     np.ndarray       ):
      __lowerCamelCase	:	str   	=			np.array(_a      ,     dtype=np.floataa       )

    __lowerCamelCase	:	Tuple   	=			1000 * 0.25 * np.log(sigmas + 1e-44       )

    return timesteps




  def 						_lowercase					(			self				:     Union[str, Any]      ,     _a				:     Optional[int] = None      ,     _a				:     Union[str, torch.device] = None      ,     _a				:     Optional[List[int]] = None      ,     ) -> List[str]:
    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(_a       )       ):
        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	:	List[Any]   	=			np.array(_a      ,     dtype=np.intaa       )
      __lowerCamelCase	:	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.'       )

      __lowerCamelCase	:	Optional[Any]   	=			num_inference_steps

      __lowerCamelCase	:	Any   	=			self.config.num_train_timesteps // self.num_inference_steps
      __lowerCamelCase	:	List[Any]   	=			(np.arange(0      ,     _a       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
      __lowerCamelCase	:	Tuple   	=			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	:	str   	=			self.config.num_train_timesteps
    __lowerCamelCase	:	Optional[int]   	=			timesteps[::-1].copy()
    __lowerCamelCase	:	Tuple   	=			ramp / (num_train_timesteps - 1)
    __lowerCamelCase	:	Tuple   	=			self._convert_to_karras(_a       )
    __lowerCamelCase	:	Union[str, Any]   	=			self.sigma_to_t(_a       )

    __lowerCamelCase	:	List[Any]   	=			np.concatenate([sigmas, [self.sigma_min]]       ).astype(np.floataa       )
    __lowerCamelCase	:	Dict   	=			torch.from_numpy(_a       ).to(device=_a       )

    if str(_a       ).startswith('mps'       ):
      # mps does not support float64
      __lowerCamelCase	:	str   	=			torch.from_numpy(_a       ).to(_a      ,     dtype=torch.floataa       )
    else:
      __lowerCamelCase	:	List[Any]   	=			torch.from_numpy(_a       ).to(device=_a       )




  def 						_lowercase					(			self				:     str      ,     _a				:     List[str]       ) -> str:
    __lowerCamelCase	:	float   	=			self.config.sigma_min
    __lowerCamelCase	:	float   	=			self.config.sigma_max

    __lowerCamelCase	:	Optional[Any]   	=			self.config.rho
    __lowerCamelCase	:	Any   	=			sigma_min ** (1 / rho)
    __lowerCamelCase	:	List[str]   	=			sigma_max ** (1 / rho)
    __lowerCamelCase	:	str   	=			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas




  def 						_lowercase					(			self				:     str      ,     _a				:     Optional[int]       ) -> Optional[Any]:
    __lowerCamelCase	:	Optional[Any]   	=			self.config.sigma_data

    __lowerCamelCase	:	List[str]   	=			sigma_data**2 / (sigma**2 + sigma_data**2)
    __lowerCamelCase	:	Union[str, Any]   	=			sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
    return c_skip, c_out




  def 						_lowercase					(			self				:     Optional[int]      ,     _a				:     Any       ) -> Union[str, Any]:
    __lowerCamelCase	:	Optional[int]   	=			self.config.sigma_min
    __lowerCamelCase	:	Any   	=			self.config.sigma_data

    __lowerCamelCase	:	Optional[int]   	=			sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
    __lowerCamelCase	:	Optional[Any]   	=			(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
    return c_skip, c_out




  def 						_lowercase					(			self				:     Any      ,     _a				:     torch.FloatTensor      ,     _a				:     Union[float, torch.FloatTensor]      ,     _a				:     torch.FloatTensor      ,     _a				:     Optional[torch.Generator] = None      ,     _a				:     bool = True      ,     ) -> Union[CMStochasticIterativeSchedulerOutput, Tuple]:

    if (
        isinstance(_a      ,     _a       )
        or isinstance(_a      ,     torch.IntTensor       )
        or isinstance(_a      ,     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(_a      ,     torch.Tensor       ):
      __lowerCamelCase	:	str   	=			timestep.to(self.timesteps.device       )

    __lowerCamelCase	:	Tuple   	=			self.config.sigma_min
    __lowerCamelCase	:	Optional[Any]   	=			self.config.sigma_max

    __lowerCamelCase	:	Optional[int]   	=			self.index_for_timestep(_a       )

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

    # Get scalings for boundary conditions
    __lowerCamelCase	,__lowerCamelCase	:	Union[str, Any]   	=			self.get_scalings_for_boundary_condition(_a       )

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

    __lowerCamelCase	:	str   	=			sigma_next.clamp(min=_a      ,     max=_a       )

    # 3. Return noisy sample
    # tau = sigma_hat, eps = sigma_min
    __lowerCamelCase	:	Optional[int]   	=			denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

    if not return_dict:
      return (prev_sample,)

    return CMStochasticIterativeSchedulerOutput(prev_sample=_a       )




  def 						_lowercase					(			self				:     Optional[int]      ,     _a				:     torch.FloatTensor      ,     _a				:     torch.FloatTensor      ,     _a				:     torch.FloatTensor      ,     ) -> torch.FloatTensor:
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    __lowerCamelCase	:	List[str]   	=			self.sigmas.to(device=original_samples.device      ,     dtype=original_samples.dtype       )
    if original_samples.device.type == "mps" and torch.is_floating_point(_a       ):
      # mps does not support float64
      __lowerCamelCase	:	str   	=			self.timesteps.to(original_samples.device      ,     dtype=torch.floataa       )
      __lowerCamelCase	:	List[Any]   	=			timesteps.to(original_samples.device      ,     dtype=torch.floataa       )
    else:
      __lowerCamelCase	:	Tuple   	=			self.timesteps.to(original_samples.device       )
      __lowerCamelCase	:	Optional[int]   	=			timesteps.to(original_samples.device       )

    __lowerCamelCase	:	str   	=			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

    __lowerCamelCase	:	Union[str, Any]   	=			original_samples + noise * sigma
    return noisy_samples




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











'''simple docstring'''





import unittest

from diffusers import __version__
from diffusers.utils import deprecate






class  lowerCamelCase_   (    unittest.TestCase					):
  """simple docstring"""




  a_							 =""".""".join([str(int(__version__.split("""."""					)[0]					) + 1					)] + __version__.split("""."""					)[1:]					)
  a_							 ="""0.0.1"""




  def 						_lowercase					(			self				:     Optional[Any]       ) -> int:
    __lowerCamelCase	:	Any   	=			{'deprecated_arg': 4}

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	:	Optional[int]   	=			deprecate('deprecated_arg'      ,     self.higher_version      ,     'message'      ,     take_from=_a       )

    assert output == 4
    assert (
        str(warning.warning       )
        == f'The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.'
        " message"
    )




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> Optional[int]:
    __lowerCamelCase	:	int   	=			{'deprecated_arg': 4}

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	:	Optional[Any]   	=			deprecate(('deprecated_arg', self.higher_version, 'message')      ,     take_from=_a       )

    assert output == 4
    assert (
        str(warning.warning       )
        == f'The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.'
        " message"
    )




  def 						_lowercase					(			self				:     int       ) -> Optional[Any]:
    __lowerCamelCase	:	Optional[int]   	=			{'deprecated_arg_1': 4, 'deprecated_arg_2': 8}
    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	,__lowerCamelCase	:	Any   	=			deprecate(
          ('deprecated_arg_1', self.higher_version, 'Hey')      ,     ('deprecated_arg_2', self.higher_version, 'Hey')      ,     take_from=_a      ,     )
    assert output_a == 4
    assert output_a == 8
    assert (
        str(warning.warnings[0].message       )
        == "The `deprecated_arg_1` argument is deprecated and will be removed in version"
        f' {self.higher_version}. Hey'
    )
    assert (
        str(warning.warnings[1].message       )
        == "The `deprecated_arg_2` argument is deprecated and will be removed in version"
        f' {self.higher_version}. Hey'
    )




  def 						_lowercase					(			self				:     str       ) -> Optional[Any]:
    __lowerCamelCase	:	Optional[int]   	=			{'deprecated_arg': 4}

    with self.assertRaises(_a       ) as error:
      deprecate(('wrong_arg', self.higher_version, 'message')      ,     take_from=_a       )

    assert "test_deprecate_function_incorrect_arg in" in str(error.exception       )
    assert "line" in str(error.exception       )
    assert "got an unexpected keyword argument `deprecated_arg`" in str(error.exception       )




  def 						_lowercase					(			self				:     Optional[Any]       ) -> Union[str, Any]:
    with self.assertWarns(_a       ) as warning:
      deprecate(('deprecated_arg', self.higher_version, 'message')       )

    assert (
        str(warning.warning       )
        == f'`deprecated_arg` is deprecated and will be removed in version {self.higher_version}. message'
    )




  def 						_lowercase					(			self				:     Optional[Any]       ) -> Union[str, Any]:
    with self.assertWarns(_a       ) as warning:
      deprecate(
          ('deprecated_arg_1', self.higher_version, 'Hey')      ,     ('deprecated_arg_2', self.higher_version, 'Hey')      ,     )
    assert (
        str(warning.warnings[0].message       )
        == f'`deprecated_arg_1` is deprecated and will be removed in version {self.higher_version}. Hey'
    )
    assert (
        str(warning.warnings[1].message       )
        == f'`deprecated_arg_2` is deprecated and will be removed in version {self.higher_version}. Hey'
    )




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> Dict:






    class  lowerCamelCase_   :
      """simple docstring"""




      a_							 =5

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	:	int   	=			deprecate(('arg', self.higher_version, 'message')      ,     take_from=Args()       )

    assert arg == 5
    assert (
        str(warning.warning       )
        == f'The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )




  def 						_lowercase					(			self				:     Dict       ) -> Union[str, Any]:






    class  lowerCamelCase_   :
      """simple docstring"""




      a_							 =5
      a_							 =7

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	,__lowerCamelCase	:	List[str]   	=			deprecate(
          ('arg', self.higher_version, 'message')      ,     ('foo', self.higher_version, 'message')      ,     ('does not exist', self.higher_version, 'message')      ,     take_from=Args()      ,     )

    assert arg_a == 5
    assert arg_a == 7
    assert (
        str(warning.warning       )
        == f'The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )
    assert (
        str(warning.warnings[0].message       )
        == f'The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )
    assert (
        str(warning.warnings[1].message       )
        == f'The `foo` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> List[str]:
    __lowerCamelCase	:	Union[str, Any]   	=			{'deprecated_arg': 4}

    with self.assertRaises(_a       ) as error:
      deprecate(('wrong_arg', self.lower_version, 'message')      ,     take_from=_a       )

    assert (
        str(error.exception       )
        == "The deprecation tuple ('wrong_arg', '0.0.1', 'message') should be removed since diffusers' version"
        f' {__version__} is >= {self.lower_version}'
    )




  def 						_lowercase					(			self				:     Optional[int]       ) -> str:
    with self.assertWarns(_a       ) as warning:
      deprecate(('deprecated_arg', self.higher_version, 'This message is better!!!')      ,     standard_warn=_a       )

    assert str(warning.warning       ) == "This message is better!!!"




  def 						_lowercase					(			self				:     List[Any]       ) -> Union[str, Any]:
    with self.assertWarns(_a       ) as warning:
      deprecate(('deprecated_arg', self.higher_version, 'This message is better!!!')      ,     standard_warn=_a       )
    assert str(warning.warning       ) == "This message is better!!!"
    assert "diffusers/tests/others/test_utils.py" in warning.filename






