






'''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


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





@dataclass
class  snake_case__      (							lowerCamelCase__   ):
	lowercase__		:  Optional[Any]							   =					42





class  snake_case__      (							lowerCamelCase__		,						lowerCamelCase__   ):
	lowercase__		:  Optional[Any]							   =					1
	@register_to_config
	def __init__(       self    ,     lowerCAmelCase__ = 40    ,     lowerCAmelCase__ = 0.0_0_2    ,     lowerCAmelCase__ = 8_0.0    ,     lowerCAmelCase__ = 0.5    ,     lowerCAmelCase__ = 1.0    ,     lowerCAmelCase__ = 7.0    ,     lowerCAmelCase__ = True    ,     )							->   Optional[int]:
							# standard deviation of the initial noise distribution
							__magic_name__    :     str     		=      sigma_max

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

							# setable values
							__magic_name__    :     int     		=      None
							__magic_name__    :     List[Any]     		=      torch.from_numpy(SCREAMING_SNAKE_CASE_				)
							__magic_name__    :     Tuple     		=      torch.from_numpy(SCREAMING_SNAKE_CASE_				)
							__magic_name__    :     Optional[Any]     		=      False
							__magic_name__    :     Optional[int]     		=      False
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__=None				)							->   Any:
							if schedule_timesteps is None:
													__magic_name__    :     Optional[int]     		=      self.timesteps

							__magic_name__    :     Optional[Any]     		=      (schedule_timesteps == timestep).nonzero()
							return indices.item()
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)							->   int:
							# Get sigma corresponding to timestep
							if isinstance(SCREAMING_SNAKE_CASE_    ,     torch.Tensor				):
													__magic_name__    :     List[Any]     		=      timestep.to(self.timesteps.device				)
							__magic_name__    :     str     		=      self.index_for_timestep(SCREAMING_SNAKE_CASE_				)
							__magic_name__    :     int     		=      self.sigmas[step_idx]

							__magic_name__    :     Tuple     		=      sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

							__magic_name__    :     Dict     		=      True
							return sample
	def     __magic_name__       (       self    ,     lowerCAmelCase__				)							->   Union[str, Any]:
							if not isinstance(SCREAMING_SNAKE_CASE_    ,     np.ndarray				):
													__magic_name__    :     str     		=      np.array(SCREAMING_SNAKE_CASE_    ,     dtype=np.floataa				)

							__magic_name__    :     str     		=      10_00 * 0.2_5 * np.log(sigmas + 1e-4_4				)

							return timesteps
	def     __magic_name__       (       self    ,     lowerCAmelCase__ = None    ,     lowerCAmelCase__ = None    ,     lowerCAmelCase__ = None    ,     )							->   Any:
							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(SCREAMING_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}.'				)

													__magic_name__    :     List[str]     		=      np.array(SCREAMING_SNAKE_CASE_    ,     dtype=np.intaa				)
													__magic_name__    :     int     		=      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.'				)

													__magic_name__    :     Optional[Any]     		=      num_inference_steps

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

							__magic_name__    :     str     		=      np.concatenate([sigmas, [self.sigma_min]]				).astype(np.floataa				)
							__magic_name__    :     Optional[Any]     		=      torch.from_numpy(SCREAMING_SNAKE_CASE_				).to(device=SCREAMING_SNAKE_CASE_				)

							if str(SCREAMING_SNAKE_CASE_				).startswith("""mps"""				):
													# mps does not support float64
													__magic_name__    :     str     		=      torch.from_numpy(SCREAMING_SNAKE_CASE_				).to(SCREAMING_SNAKE_CASE_    ,     dtype=torch.floataa				)
							else:
													__magic_name__    :     Any     		=      torch.from_numpy(SCREAMING_SNAKE_CASE_				).to(device=SCREAMING_SNAKE_CASE_				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__				)							->   str:
							__magic_name__    :     List[str]     		=      self.config.sigma_min
							__magic_name__    :     Optional[int]     		=      self.config.sigma_max

							__magic_name__    :     Optional[Any]     		=      self.config.rho
							__magic_name__    :     Dict     		=      sigma_min ** (1 / rho)
							__magic_name__    :     Union[str, Any]     		=      sigma_max ** (1 / rho)
							__magic_name__    :     str     		=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
							return sigmas
	def     __magic_name__       (       self    ,     lowerCAmelCase__				)							->   Optional[Any]:
							__magic_name__    :     Union[str, Any]     		=      self.config.sigma_data

							__magic_name__    :     Any     		=      sigma_data**2 / (sigma**2 + sigma_data**2)
							__magic_name__    :     Union[str, Any]     		=      sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out
	def     __magic_name__       (       self    ,     lowerCAmelCase__				)							->   Any:
							__magic_name__    :     List[str]     		=      self.config.sigma_min
							__magic_name__    :     Any     		=      self.config.sigma_data

							__magic_name__    :     Dict     		=      sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
							__magic_name__    :     int     		=      (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__ = None    ,     lowerCAmelCase__ = True    ,     )							->   int:

							if (
							    isinstance(SCREAMING_SNAKE_CASE_    ,     SCREAMING_SNAKE_CASE_				)
							    or isinstance(SCREAMING_SNAKE_CASE_    ,     torch.IntTensor				)
							    or isinstance(SCREAMING_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(SCREAMING_SNAKE_CASE_    ,     torch.Tensor				):
													__magic_name__    :     Optional[int]     		=      timestep.to(self.timesteps.device				)

							__magic_name__    :     int     		=      self.config.sigma_min
							__magic_name__    :     int     		=      self.config.sigma_max

							__magic_name__    :     int     		=      self.index_for_timestep(SCREAMING_SNAKE_CASE_				)

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

							# Get scalings for boundary conditions
							__magic_name__		,__magic_name__    :     int     		=      self.get_scalings_for_boundary_condition(SCREAMING_SNAKE_CASE_				)

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

							__magic_name__    :     Union[str, Any]     		=      sigma_next.clamp(min=SCREAMING_SNAKE_CASE_    ,     max=SCREAMING_SNAKE_CASE_				)

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

							if not return_dict:
													return (prev_sample,)

							return CMStochasticIterativeSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   List[Any]:
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							__magic_name__    :     List[Any]     		=      self.sigmas.to(device=original_samples.device    ,     dtype=original_samples.dtype				)
							if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_				):
													# mps does not support float64
													__magic_name__    :     Optional[int]     		=      self.timesteps.to(original_samples.device    ,     dtype=torch.floataa				)
													__magic_name__    :     str     		=      timesteps.to(original_samples.device    ,     dtype=torch.floataa				)
							else:
													__magic_name__    :     List[Any]     		=      self.timesteps.to(original_samples.device				)
													__magic_name__    :     Optional[int]     		=      timesteps.to(original_samples.device				)

							__magic_name__    :     int     		=      [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

							__magic_name__    :     str     		=      original_samples + noise * sigma
							return noisy_samples







	def __len__(       self				)							->   Dict:
							return self.config.num_train_timesteps








def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						__magic_name__    :     List[str]     		=      float()
						try:
												__magic_name__    :     Union[str, Any]     		=      mass * acceleration
						except Exception:
												return -0.0
						return force


if __name__ == "__main__":
		import doctest

		# run doctest
		doctest.testmod()

		# demo
		__magic_name__:  List[Any]  	=			12.5
		__magic_name__:  Union[str, Any]  	=			10
		__magic_name__:  Optional[int]  	=			newtons_second_law_of_motion(mass, acceleration)
		print("The force is ", force, "N")
