

'''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


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



@dataclass
class        lowercase    (	a_			):
							_lowerCamelCase		:   torch.FloatTensor



class        lowercase    (	a_,       a_			):
							_lowerCamelCase		:   str= 1


							@register_to_config
							def __init__(      self     ,      _snake_case = 40     ,      _snake_case = 0.002     ,      _snake_case = 80.0     ,      _snake_case = 0.5     ,      _snake_case = 1.0     ,      _snake_case = 7.0     ,      _snake_case = True     ,      )				->					Union[str, Any]:
								# standard deviation of the initial noise distribution
								UpperCAmelCase_		:   Optional[int]													=       sigma_max

								UpperCAmelCase_		:   List[Any]													=       np.linspace(0     ,      1     ,      _snake_case)
								UpperCAmelCase_		:   Any													=       self._convert_to_karras(_snake_case)
								UpperCAmelCase_		:   List[str]													=       self.sigma_to_t(_snake_case)

								# setable values
								UpperCAmelCase_		:   Dict													=       None
								UpperCAmelCase_		:   Tuple													=       torch.from_numpy(_snake_case)
								UpperCAmelCase_		:   Any													=       torch.from_numpy(_snake_case)
								UpperCAmelCase_		:   Tuple													=       False
								UpperCAmelCase_		:   Optional[Any]													=       False


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case=None)				->					Optional[Any]:
								if schedule_timesteps is None:
									UpperCAmelCase_		:   str													=       self.timesteps

								UpperCAmelCase_		:   List[str]													=       (schedule_timesteps == timestep).nonzero()
								return indices.item()


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					torch.FloatTensor:
								# Get sigma corresponding to timestep
								if isinstance(_snake_case     ,      torch.Tensor):
									UpperCAmelCase_		:   str													=       timestep.to(self.timesteps.device)
								UpperCAmelCase_		:   List[str]													=       self.index_for_timestep(_snake_case)
								UpperCAmelCase_		:   Tuple													=       self.sigmas[step_idx]

								UpperCAmelCase_		:   List[Any]													=       sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

								UpperCAmelCase_		:   Optional[Any]													=       True
								return sample


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

								UpperCAmelCase_		:   Optional[int]													=       1000 * 0.25 * np.log(sigmas + 1e-44)

								return timesteps


							def     _snake_case				(      self     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = None     ,      )				->					List[str]:
								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}.""")

									UpperCAmelCase_		:   List[Any]													=       np.array(_snake_case     ,      dtype=np.intaa)
									UpperCAmelCase_		:   Optional[Any]													=       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.""")

									UpperCAmelCase_		:   str													=       num_inference_steps

									UpperCAmelCase_		:   Union[str, Any]													=       self.config.num_train_timesteps // self.num_inference_steps
									UpperCAmelCase_		:   str													=       (np.arange(0     ,      _snake_case) * step_ratio).round()[::-1].copy().astype(np.intaa)
									UpperCAmelCase_		:   Tuple													=       False

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

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

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


							def     _snake_case				(      self     ,      _snake_case)				->					int:
								UpperCAmelCase_		:   float													=       self.config.sigma_min
								UpperCAmelCase_		:   float													=       self.config.sigma_max

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


							def     _snake_case				(      self     ,      _snake_case)				->					List[Any]:
								UpperCAmelCase_		:   int													=       self.config.sigma_data

								UpperCAmelCase_		:   Union[str, Any]													=       sigma_data**2 / (sigma**2 + sigma_data**2)
								UpperCAmelCase_		:   List[Any]													=       sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
								return c_skip, c_out


							def     _snake_case				(      self     ,      _snake_case)				->					Dict:
								UpperCAmelCase_		:   List[str]													=       self.config.sigma_min
								UpperCAmelCase_		:   List[str]													=       self.config.sigma_data

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


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = True     ,      )				->					Union[CMStochasticIterativeSchedulerOutput, Tuple]:

								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):
									UpperCAmelCase_		:   Union[str, Any]													=       timestep.to(self.timesteps.device)

								UpperCAmelCase_		:   Optional[int]													=       self.config.sigma_min
								UpperCAmelCase_		:   Tuple													=       self.config.sigma_max

								UpperCAmelCase_		:   Optional[Any]													=       self.index_for_timestep(_snake_case)

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

								# Get scalings for boundary conditions
								UpperCAmelCase_						,     UpperCAmelCase_		:   Dict													=       self.get_scalings_for_boundary_condition(_snake_case)

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

								UpperCAmelCase_		:   Union[str, Any]													=       sigma_next.clamp(min=_snake_case     ,      max=_snake_case)

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

								if not return_dict:
									return (prev_sample,)

								return CMStochasticIterativeSchedulerOutput(prev_sample=_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								UpperCAmelCase_		:   Any													=       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
									UpperCAmelCase_		:   List[Any]													=       self.timesteps.to(original_samples.device     ,      dtype=torch.floataa)
									UpperCAmelCase_		:   List[str]													=       timesteps.to(original_samples.device     ,      dtype=torch.floataa)
								else:
									UpperCAmelCase_		:   str													=       self.timesteps.to(original_samples.device)
									UpperCAmelCase_		:   Tuple													=       timesteps.to(original_samples.device)

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

								UpperCAmelCase_		:   Union[str, Any]													=       sigmas[step_indices].flatten()
								while len(sigma.shape) < len(original_samples.shape):
									UpperCAmelCase_		:   List[str]													=       sigma.unsqueeze(-1)

								UpperCAmelCase_		:   Union[str, Any]													=       original_samples + noise * sigma
								return noisy_samples





							def __len__(      self)				->					Union[str, Any]:
								return self.config.num_train_timesteps









'''simple docstring'''


def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     int:
	if not isinstance(UpperCamelCase ,UpperCamelCase	):
		raise TypeError('recursive_lucas_number accepts only integer arguments.'	)
	if n_th_number == 0:
		return 2
	if n_th_number == 1:
		return 1

	return recursive_lucas_number(n_th_number - 1	) + recursive_lucas_number(
	    n_th_number - 2	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     int:
	if not isinstance(UpperCamelCase ,UpperCamelCase	):
		raise TypeError('dynamic_lucas_number accepts only integer arguments.'	)
	UpperCAmelCase_						,     UpperCAmelCase_		:   List[str]													=       2, 1
	for _ in range(UpperCamelCase	):
		UpperCAmelCase_						,     UpperCAmelCase_		:   Dict													=       b, a + b
	return a


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

						testmod()
						lowerCAmelCase__      				=			int(input("Enter the number of terms in lucas series:\n").strip())
						print("Using recursive function to calculate lucas series:")
						print(" ".join(str(recursive_lucas_number(i)) for i in range(n)))
						print("\nUsing dynamic function to calculate lucas series:")
						print(" ".join(str(dynamic_lucas_number(i)) for i in range(n)))






