"""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 					_UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:torch.FloatTensor



class 					_UpperCAmelCase		(_UpperCAmelCase					,_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:Any							=				1






    @register_to_config
    def __init__(     self					,    _UpperCAmelCase = 40					,    _UpperCAmelCase = 0.002					,    _UpperCAmelCase = 80.0					,    _UpperCAmelCase = 0.5					,    _UpperCAmelCase = 1.0					,    _UpperCAmelCase = 7.0					,    _UpperCAmelCase = True					,    ):
       # standard deviation of the initial noise distribution
       lowercase__:     Optional[int]      =   sigma_max

       lowercase__:     List[str]      =   np.linspace(0					,    1					,    _UpperCAmelCase							)
       lowercase__:     List[Any]      =   self._convert_to_karras(_UpperCAmelCase							)
       lowercase__:     Optional[int]      =   self.sigma_to_t(_UpperCAmelCase							)

       # setable values
       lowercase__:     Dict      =   None
       lowercase__:     Optional[int]      =   torch.from_numpy(_UpperCAmelCase							)
       lowercase__:     List[Any]      =   torch.from_numpy(_UpperCAmelCase							)
       lowercase__:     List[Any]      =   False
       lowercase__:     List[Any]      =   False






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase=None							):
       if schedule_timesteps is None:
          lowercase__:     int      =   self.timesteps

       lowercase__:     Optional[Any]      =   (schedule_timesteps == timestep).nonzero()
       return indices.item()






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase							):
       # Get sigma corresponding to timestep
       if isinstance(_UpperCAmelCase					,    torch.Tensor							):
          lowercase__:     Optional[Any]      =   timestep.to(self.timesteps.device							)
       lowercase__:     int      =   self.index_for_timestep(_UpperCAmelCase							)
       lowercase__:     List[Any]      =   self.sigmas[step_idx]

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

       lowercase__:     Optional[int]      =   True
       return sample






    def _snake_case    (     self					,    _UpperCAmelCase							):
       if not isinstance(_UpperCAmelCase					,    np.ndarray							):
          lowercase__:     List[str]      =   np.array(_UpperCAmelCase					,    dtype=np.floataa							)

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

       return timesteps






    def _snake_case    (     self					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = 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__:     str      =   np.array(_UpperCAmelCase					,    dtype=np.intaa							)
          lowercase__:     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."""							)

          lowercase__:     Tuple      =   num_inference_steps

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

       lowercase__:     Optional[int]      =   np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
       lowercase__:     int      =   torch.from_numpy(_UpperCAmelCase							).to(device=_UpperCAmelCase							)

       if str(_UpperCAmelCase							).startswith('''mps'''							):
          # mps does not support float64
          lowercase__:     Optional[int]      =   torch.from_numpy(_UpperCAmelCase							).to(_UpperCAmelCase					,    dtype=torch.floataa							)
       else:
          lowercase__:     Tuple      =   torch.from_numpy(_UpperCAmelCase							).to(device=_UpperCAmelCase							)






    def _snake_case    (     self					,    _UpperCAmelCase							):
       lowercase__:     float      =   self.config.sigma_min
       lowercase__:     float      =   self.config.sigma_max

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






    def _snake_case    (     self					,    _UpperCAmelCase							):
       lowercase__:     Tuple      =   self.config.sigma_data

       lowercase__:     int      =   sigma_data**2 / (sigma**2 + sigma_data**2)
       lowercase__:     Tuple      =   sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out






    def _snake_case    (     self					,    _UpperCAmelCase							):
       lowercase__:     Tuple      =   self.config.sigma_min
       lowercase__:     Any      =   self.config.sigma_data

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






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = None					,    _UpperCAmelCase = 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__:     List[Any]      =   timestep.to(self.timesteps.device							)

       lowercase__:     int      =   self.config.sigma_min
       lowercase__:     List[Any]      =   self.config.sigma_max

       lowercase__:     Dict      =   self.index_for_timestep(_UpperCAmelCase							)

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

       # Get scalings for boundary conditions
       lowercase__:     int      =   self.get_scalings_for_boundary_condition(_UpperCAmelCase							)

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

       lowercase__:     Dict      =   sigma_next.clamp(min=_UpperCAmelCase					,    max=_UpperCAmelCase							)

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

       if not return_dict:
          return (prev_sample,)

       return CMStochasticIterativeSchedulerOutput(prev_sample=_UpperCAmelCase							)






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    ):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       lowercase__:     Tuple      =   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__:     Dict      =   self.timesteps.to(original_samples.device					,    dtype=torch.floataa							)
          lowercase__:     str      =   timesteps.to(original_samples.device					,    dtype=torch.floataa							)
       else:
          lowercase__:     Any      =   self.timesteps.to(original_samples.device							)
          lowercase__:     int      =   timesteps.to(original_samples.device							)

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

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

       lowercase__:     int      =   original_samples + noise * sigma
       return noisy_samples






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

"""simple docstring"""






def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						Optional[int]:
   lowercase__:     str      =   [7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
   for gap in gaps:
      for i in range(__UpperCAmelCase ,					len(__UpperCAmelCase				)				):
         lowercase__:     Any      =   collection[i]
         lowercase__:     Tuple      =   i
         while j >= gap and collection[j - gap] > insert_value:
            lowercase__:     List[str]      =   collection[j - gap]
            j -= gap
         if j != i:
            lowercase__:     List[str]      =   insert_value
   return collection


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       __A         =				input("Enter numbers separated by a comma:\n").strip()
       __A         =				[int(item) for item in user_input.split(",")]
       print(shell_sort(unsorted))
