


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


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




@dataclass
class A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						torch.FloatTensor




class A						(     UpperCamelCase_					,							UpperCamelCase_  ):
      UpperCamelCase__							:						List[str]   =1






      @register_to_config
      def __init__(      self		:  List[str]    ,     lowercase_		:  int = 40    ,     lowercase_		:  float = 0.002    ,     lowercase_		:  float = 80.0    ,     lowercase_		:  float = 0.5    ,     lowercase_		:  float = 1.0    ,     lowercase_		:  float = 7.0    ,     lowercase_		:  bool = True    ,     )      ->		str:
             """simple docstring"""




             _lowerCamelCase :	Union[str, Any]						=sigma_max

             _lowerCamelCase :	Tuple						=np.linspace(0    ,     1    ,     lowercase_				)
             _lowerCamelCase :	Union[str, Any]						=self._convert_to_karras(lowercase_				)
             _lowerCamelCase :	Any						=self.sigma_to_t(lowercase_				)

             # setable values
             _lowerCamelCase :	Any						=None
             _lowerCamelCase :	Union[str, Any]						=torch.from_numpy(lowercase_				)
             _lowerCamelCase :	Union[str, Any]						=torch.from_numpy(lowercase_				)
             _lowerCamelCase :	str						=False
             _lowerCamelCase :	Union[str, Any]						=False






      def  lowerCamelCase (      self		:  Any    ,     lowercase_		:  List[str]    ,     lowercase_		:  Tuple=None				)      ->		Union[str, Any]:
             """simple docstring"""




             if schedule_timesteps is None:
                    _lowerCamelCase :	List[Any]						=self.timesteps

             _lowerCamelCase :	int						=(schedule_timesteps == timestep).nonzero()
             return indices.item()






      def  lowerCamelCase (      self		:  Any    ,     lowercase_		:  torch.FloatTensor    ,     lowercase_		:  Union[float, torch.FloatTensor]				)      ->		torch.FloatTensor:
             """simple docstring"""




             if isinstance(lowercase_    ,     torch.Tensor				):
                    _lowerCamelCase :	Optional[Any]						=timestep.to(self.timesteps.device				)
             _lowerCamelCase :	int						=self.index_for_timestep(lowercase_				)
             _lowerCamelCase :	int						=self.sigmas[step_idx]

             _lowerCamelCase :	Optional[int]						=sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

             _lowerCamelCase :	str						=True
             return sample






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  Union[float, np.ndarray]				)      ->		Optional[int]:
             """simple docstring"""




             if not isinstance(lowercase_    ,     np.ndarray				):
                    _lowerCamelCase :	List[str]						=np.array(lowercase_    ,     dtype=np.floataa				)

             _lowerCamelCase :	Any						=1000 * 0.25 * np.log(sigmas + 1E-44				)

             return timesteps






      def  lowerCamelCase (      self		:  int    ,     lowercase_		:  Optional[int] = None    ,     lowercase_		:  Union[str, torch.device] = None    ,     lowercase_		:  Optional[List[int]] = None    ,     )      ->		Optional[Any]:
             """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(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}.'''				)

                    _lowerCamelCase :	int						=np.array(lowercase_    ,     dtype=np.intaa				)
                    _lowerCamelCase :	str						=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 :	Any						=num_inference_steps

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

             _lowerCamelCase :	int						=np.concatenate([sigmas, [self.sigma_min]]				).astype(np.floataa				)
             _lowerCamelCase :	List[Any]						=torch.from_numpy(lowercase_				).to(device=lowercase_				)

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






      def  lowerCamelCase (      self		:  List[Any]    ,     lowercase_		:  Optional[Any]				)      ->		str:
             """simple docstring"""




             _lowerCamelCase :	float						=self.config.sigma_min
             _lowerCamelCase :	float						=self.config.sigma_max

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






      def  lowerCamelCase (      self		:  Tuple    ,     lowercase_		:  Union[str, Any]				)      ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase :	Optional[int]						=self.config.sigma_data

             _lowerCamelCase :	List[Any]						=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  lowerCamelCase (      self		:  Optional[Any]    ,     lowercase_		:  List[Any]				)      ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase :	str						=self.config.sigma_min
             _lowerCamelCase :	Tuple						=self.config.sigma_data

             _lowerCamelCase :	Union[str, Any]						=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  lowerCamelCase (      self		:  int    ,     lowercase_		:  torch.FloatTensor    ,     lowercase_		:  Union[float, torch.FloatTensor]    ,     lowercase_		:  torch.FloatTensor    ,     lowercase_		:  Optional[torch.Generator] = None    ,     lowercase_		:  bool = True    ,     )      ->		Union[CMStochasticIterativeSchedulerOutput, Tuple]:
             """simple docstring"""




             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				):
                    _lowerCamelCase :	Tuple						=timestep.to(self.timesteps.device				)

             _lowerCamelCase :	List[str]						=self.config.sigma_min
             _lowerCamelCase :	Dict						=self.config.sigma_max

             _lowerCamelCase :	Union[str, Any]						=self.index_for_timestep(lowercase_				)

             # sigma_next corresponds to next_t in original implementation
             _lowerCamelCase :	int						=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 :	Dict						=self.sigmas[-1]

             # Get scalings for boundary conditions
             _lowerCamelCase			,  _lowerCamelCase :	Dict						=self.get_scalings_for_boundary_condition(lowercase_				)

             # 1. Denoise model output using boundary conditions
             _lowerCamelCase :	str						=c_out * model_output + c_skip * sample
             if self.config.clip_denoised:
                    _lowerCamelCase :	Tuple						=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 :	str						=randn_tensor(
                        model_output.shape    ,     dtype=model_output.dtype    ,     device=model_output.device    ,     generator=lowercase_				)
             else:
                    _lowerCamelCase :	str						=torch.zeros_like(lowercase_				)
             _lowerCamelCase :	List[str]						=noise * self.config.s_noise

             _lowerCamelCase :	List[str]						=sigma_next.clamp(min=lowercase_    ,     max=lowercase_				)

             # 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=lowercase_				)






      def  lowerCamelCase (      self		:  Optional[Any]    ,     lowercase_		:  torch.FloatTensor    ,     lowercase_		:  torch.FloatTensor    ,     lowercase_		:  torch.FloatTensor    ,     )      ->		torch.FloatTensor:
             """simple docstring"""




             _lowerCamelCase :	Union[str, Any]						=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
                    _lowerCamelCase :	Tuple						=self.timesteps.to(original_samples.device    ,     dtype=torch.floataa				)
                    _lowerCamelCase :	Tuple						=timesteps.to(original_samples.device    ,     dtype=torch.floataa				)
             else:
                    _lowerCamelCase :	Any						=self.timesteps.to(original_samples.device				)
                    _lowerCamelCase :	List[str]						=timesteps.to(original_samples.device				)

             _lowerCamelCase :	Any						=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

             _lowerCamelCase :	Optional[int]						=original_samples + noise * sigma
             return noisy_samples






      def __len__(      self		:  int				)      ->		Optional[int]:
             """simple docstring"""




             return self.config.num_train_timesteps






from __future__ import annotations

from collections.abc import Generator




def 			a_   (					SCREAMING_SNAKE_CASE__      :   int  ):
       '''simple docstring'''





       if not isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ) or n < 1:
              raise Exception('Sequence only defined for positive integers'  )

       yield n
       while n != 1:
              if n % 2 == 0:
                     n //= 2
              else:
                     _lowerCamelCase :	Optional[int]						=3 * n + 1
              yield n




def 			a_   (					):
       '''simple docstring'''





       _lowerCamelCase :	Dict						=43
       _lowerCamelCase :	Dict						=tuple(collatz_sequence(SCREAMING_SNAKE_CASE__  )  )
       print(SCREAMING_SNAKE_CASE__  )
       print(F'''Collatz sequence from {n} took {len(SCREAMING_SNAKE_CASE__  )} steps.'''  )


if __name__ == "__main__":
 main()


