




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



@dataclass
class       lowercase_		(snake_case__   ):
						lowerCAmelCase__ 			=42



class       lowercase_		(snake_case__       ,  snake_case__   ):
						lowerCAmelCase__ 			=1



						@register_to_config
						def __init__( self  :      Tuple				,       snake_case__  :      Optional[int] = 40				,       snake_case__  :      int = 0.0_02				,       snake_case__  :      Tuple = 80.0				,       snake_case__  :      List[Any] = 0.5				,       snake_case__  :      str = 1.0				,       snake_case__  :      Optional[int] = 7.0				,       snake_case__  :      Optional[int] = True				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       sigma_max

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

								# setable values
								SCREAMING_SNAKE_CASE_							=       None
								SCREAMING_SNAKE_CASE_							=       torch.from_numpy(__snake_case     )
								SCREAMING_SNAKE_CASE_							=       torch.from_numpy(__snake_case     )
								SCREAMING_SNAKE_CASE_							=       False
								SCREAMING_SNAKE_CASE_							=       False



						def   __a				( self  :      Optional[int]				,       snake_case__  :      Tuple				,       snake_case__  :      List[Any]=None     ):

								"""simple docstring"""

								if schedule_timesteps is None:
										SCREAMING_SNAKE_CASE_							=       self.timesteps

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



						def   __a				( self  :      str				,       snake_case__  :      Tuple				,       snake_case__  :      Any     ):

								"""simple docstring"""

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

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

								SCREAMING_SNAKE_CASE_							=       True
								return sample



						def   __a				( self  :      List[Any]				,       snake_case__  :      Any     ):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_							=       10_00 * 0.25 * np.log(sigmas + 1e-44     )

								return timesteps



						def   __a				( self  :      Dict				,       snake_case__  :      str = None				,       snake_case__  :      Dict = None				,       snake_case__  :      Tuple = 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(__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}.'''     )

										SCREAMING_SNAKE_CASE_							=       np.array(__snake_case				,       dtype=np.intaa     )
										SCREAMING_SNAKE_CASE_							=       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.'''     )

										SCREAMING_SNAKE_CASE_							=       num_inference_steps

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

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

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

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



						def   __a				( self  :      Optional[int]				,       snake_case__  :      Optional[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.config.sigma_min
								SCREAMING_SNAKE_CASE_							=       self.config.sigma_max

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



						def   __a				( self  :      Tuple				,       snake_case__  :      Optional[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.config.sigma_data

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



						def   __a				( self  :      Optional[int]				,       snake_case__  :      int     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.config.sigma_min
								SCREAMING_SNAKE_CASE_							=       self.config.sigma_data

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



						def   __a				( self  :      str				,       snake_case__  :      Dict				,       snake_case__  :      Any				,       snake_case__  :      Optional[int]				,       snake_case__  :      int = None				,       snake_case__  :      Optional[Any] = True				,       ):

								"""simple docstring"""

								if (
								    isinstance(__snake_case				,       __snake_case     )
								    or isinstance(__snake_case				,       torch.IntTensor     )
								    or isinstance(__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(__snake_case				,       torch.Tensor     ):
										SCREAMING_SNAKE_CASE_							=       timestep.to(self.timesteps.device     )

								SCREAMING_SNAKE_CASE_							=       self.config.sigma_min
								SCREAMING_SNAKE_CASE_							=       self.config.sigma_max

								SCREAMING_SNAKE_CASE_							=       self.index_for_timestep(__snake_case     )

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

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

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

								SCREAMING_SNAKE_CASE_							=       sigma_next.clamp(min=__snake_case				,       max=__snake_case     )

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

								if not return_dict:
										return (prev_sample,)

								return CMStochasticIterativeSchedulerOutput(prev_sample=__snake_case     )



						def   __a				( self  :      Dict				,       snake_case__  :      Optional[Any]				,       snake_case__  :      List[str]				,       snake_case__  :      List[Any]				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.sigmas.to(device=original_samples.device				,       dtype=original_samples.dtype     )
								if original_samples.device.type == "mps" and torch.is_floating_point(__snake_case     ):
										# mps does not support float64
										SCREAMING_SNAKE_CASE_							=       self.timesteps.to(original_samples.device				,       dtype=torch.floataa     )
										SCREAMING_SNAKE_CASE_							=       timesteps.to(original_samples.device				,       dtype=torch.floataa     )
								else:
										SCREAMING_SNAKE_CASE_							=       self.timesteps.to(original_samples.device     )
										SCREAMING_SNAKE_CASE_							=       timesteps.to(original_samples.device     )

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

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

								SCREAMING_SNAKE_CASE_							=       original_samples + noise * sigma
								return noisy_samples






						def __len__( self  :      Tuple     ):

								"""simple docstring"""

								return self.config.num_train_timesteps











from __future__ import annotations


def 				_a       (				lowerCAmelCase   )->  list[int]:
		if not list_of_ints:
				return []

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

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


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  list[int]:
		if bit_position == 0 or len(lowerCAmelCase   ) in [0, 1]:
				return list_of_ints

		SCREAMING_SNAKE_CASE_							=       []
		SCREAMING_SNAKE_CASE_							=       []
		# 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(lowerCAmelCase   )
				else:
						# number has a zero at bit bit_position
						zeros.append(lowerCAmelCase   )

    # recursively split both lists further
		SCREAMING_SNAKE_CASE_							=       _msd_radix_sort(lowerCAmelCase						,	bit_position - 1   )
		SCREAMING_SNAKE_CASE_							=       _msd_radix_sort(lowerCAmelCase						,	bit_position - 1   )

		# recombine lists
		SCREAMING_SNAKE_CASE_							=       zeros
		res.extend(lowerCAmelCase   )

		return res


def 				_a       (				lowerCAmelCase   )->  Dict:
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		if not list_of_ints or length == 1:
				return

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

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


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  Optional[int]:
		if bit_position == 0 or end_index - begin_index <= 1:
				return

		bit_position -= 1

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

				if changed:
						continue

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

		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )
		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()