

'''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       ( A				):
       lowerCAmelCase_				=	42






class 					lowerCAmelCase       ( A				,     A				):
       lowerCAmelCase_				=	1







       @register_to_config
       def __init__(							self     :	List[str]  , __lowercase     :	int = 40  , __lowercase     :	float = 0.0_0_2  , __lowercase     :	float = 8_0.0  , __lowercase     :	float = 0.5  , __lowercase     :	float = 1.0  , __lowercase     :	float = 7.0  , __lowercase     :	bool = True  , ):







            """simple docstring"""







            __lowercase     						=sigma_max

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

            # setable values
            __lowercase     						=None
            __lowercase     						=torch.from_numpy(__lowercase )
            __lowercase     						=torch.from_numpy(__lowercase )
            __lowercase     						=False
            __lowercase     						=False







       def      snake_case				(							self     :	int  , __lowercase     :	Optional[int]  , __lowercase     :	Optional[Any]=None ):







            """simple docstring"""







            if schedule_timesteps is None:
                 __lowercase     						=self.timesteps

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







       def      snake_case				(							self     :	Tuple  , __lowercase     :	torch.FloatTensor  , __lowercase     :	Union[float, torch.FloatTensor] ):







            """simple docstring"""







            if isinstance(__lowercase  , torch.Tensor ):
                 __lowercase     						=timestep.to(self.timesteps.device )
            __lowercase     						=self.index_for_timestep(__lowercase )
            __lowercase     						=self.sigmas[step_idx]

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

            __lowercase     						=True
            return sample







       def      snake_case				(							self     :	Optional[Any]  , __lowercase     :	Union[float, np.ndarray] ):







            """simple docstring"""







            if not isinstance(__lowercase  , np.ndarray ):
                 __lowercase     						=np.array(__lowercase  , dtype=np.floataa )

            __lowercase     						=1000 * 0.2_5 * np.log(sigmas + 1E-44 )

            return timesteps







       def      snake_case				(							self     :	Optional[Any]  , __lowercase     :	Optional[int] = None  , __lowercase     :	Union[str, torch.device] = None  , __lowercase     :	Optional[List[int]] = None  , ):







            """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}.''' )

                 __lowercase     						=np.array(__lowercase  , dtype=np.intaa )
                 __lowercase     						=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     						=num_inference_steps

                 __lowercase     						=self.config.num_train_timesteps // self.num_inference_steps
                 __lowercase     						=(np.arange(0  , __lowercase ) * step_ratio).round()[::-1].copy().astype(np.intaa )
                 __lowercase     						=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     						=self.config.num_train_timesteps
            __lowercase     						=timesteps[::-1].copy()
            __lowercase     						=ramp / (num_train_timesteps - 1)
            __lowercase     						=self._convert_to_karras(__lowercase )
            __lowercase     						=self.sigma_to_t(__lowercase )

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

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







       def      snake_case				(							self     :	Dict  , __lowercase     :	int ):







            """simple docstring"""







            __lowercase     						=self.config.sigma_min
            __lowercase     						=self.config.sigma_max

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







       def      snake_case				(							self     :	int  , __lowercase     :	int ):







            """simple docstring"""







            __lowercase     						=self.config.sigma_data

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







       def      snake_case				(							self     :	Optional[Any]  , __lowercase     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=self.config.sigma_min
            __lowercase     						=self.config.sigma_data

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







       def      snake_case				(							self     :	Tuple  , __lowercase     :	torch.FloatTensor  , __lowercase     :	Union[float, torch.FloatTensor]  , __lowercase     :	torch.FloatTensor  , __lowercase     :	Optional[torch.Generator] = None  , __lowercase     :	bool = True  , ):







            """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 ):
                 __lowercase     						=timestep.to(self.timesteps.device )

            __lowercase     						=self.config.sigma_min
            __lowercase     						=self.config.sigma_max

            __lowercase     						=self.index_for_timestep(__lowercase )

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

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

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

            __lowercase     						=sigma_next.clamp(min=__lowercase  , max=__lowercase )

            # 3. Return noisy sample
            # tau = sigma_hat, eps = sigma_min
            __lowercase     						=denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

            if not return_dict:
                 return (prev_sample,)

            return CMStochasticIterativeSchedulerOutput(prev_sample=__lowercase )







       def      snake_case				(							self     :	int  , __lowercase     :	torch.FloatTensor  , __lowercase     :	torch.FloatTensor  , __lowercase     :	torch.FloatTensor  , ):







            """simple docstring"""







            __lowercase     						=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
                 __lowercase     						=self.timesteps.to(original_samples.device  , dtype=torch.floataa )
                 __lowercase     						=timesteps.to(original_samples.device  , dtype=torch.floataa )
            else:
                 __lowercase     						=self.timesteps.to(original_samples.device )
                 __lowercase     						=timesteps.to(original_samples.device )

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

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

            __lowercase     						=original_samples + noise * sigma
            return noisy_samples







       def __len__(							self     :	Optional[int] ):







            """simple docstring"""







            return self.config.num_train_timesteps





'''simple docstring'''

from __future__ import annotations

def 			__UpperCamelCase							(		lowercase__  :					list[int]	):






     '''simple docstring'''





     if not list_of_ints:
          return []

     if min(lowercase__	) < 0:
          raise ValueError('All numbers must be positive'	)

     __lowercase     						=max(len(bin(lowercase__	)[2:]	) for x in list_of_ints	)
     return _msd_radix_sort(lowercase__,   lowercase__	)

def 			__UpperCamelCase							(		lowercase__  :					list[int],   lowercase__  :					int	):






     '''simple docstring'''





     if bit_position == 0 or len(lowercase__	) in [0, 1]:
          return list_of_ints

     __lowercase     						=[]
     __lowercase     						=[]
     # Split numbers based on bit at bit_position from the right
     for number in list_of_ints:
          if (number >> (bit_position - 1)) & 1:
               # number has a one at bit bit_position
               ones.append(lowercase__	)
          else:
               # number has a zero at bit bit_position
               zeros.append(lowercase__	)

    # recursively split both lists further
     __lowercase     						=_msd_radix_sort(lowercase__,   bit_position - 1	)
     __lowercase     						=_msd_radix_sort(lowercase__,   bit_position - 1	)

     # recombine lists
     __lowercase     						=zeros
     res.extend(lowercase__	)

     return res

def 			__UpperCamelCase							(		lowercase__  :					list[int]	):






     '''simple docstring'''





     __lowercase     						=len(lowercase__	)
     if not list_of_ints or length == 1:
          return

     if min(lowercase__	) < 0:
          raise ValueError('All numbers must be positive'	)

     __lowercase     						=max(len(bin(lowercase__	)[2:]	) for x in list_of_ints	)
     _msd_radix_sort_inplace(lowercase__,   lowercase__,   0,   lowercase__	)

def 			__UpperCamelCase							(		lowercase__  :					list[int],   lowercase__  :					int,   lowercase__  :					int,   lowercase__  :					int	):






     '''simple docstring'''





     if bit_position == 0 or end_index - begin_index <= 1:
          return

     bit_position -= 1

     __lowercase     						=begin_index
     __lowercase     						=end_index - 1
     while i <= j:
          __lowercase     						=False
          if not (list_of_ints[i] >> bit_position) & 1:
               # found zero at the beginning
               i += 1
               __lowercase     						=True
          if (list_of_ints[j] >> bit_position) & 1:
               # found one at the end
               j -= 1
               __lowercase     						=True

          if changed:
               continue

          __lowercase						,       __lowercase     						=list_of_ints[j], list_of_ints[i]
          j -= 1
          if j != i:
               i += 1

     _msd_radix_sort_inplace(lowercase__,   lowercase__,   lowercase__,   lowercase__	)
     _msd_radix_sort_inplace(lowercase__,   lowercase__,   lowercase__,   lowercase__	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


