





'''simple docstring'''


import math
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 KarrasDiffusionSchedulers, SchedulerMixin


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



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     42
		__lowerCAmelCase								=     None






def  __a			(  UpperCAmelCase							,      UpperCAmelCase=0.999							,      UpperCAmelCase="cosine"							,      )   ->Union[str, Any]:



	"""simple docstring"""







	if alpha_transform_type == "cosine":

		def alpha_bar_fn(UpperCAmelCase     ):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(UpperCAmelCase     ):
			return math.exp(t * -12.0     )

	else:
		raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""     )

	A					=   []
	for i in range(UpperCAmelCase     ):
		A					=   i / num_diffusion_timesteps
		A					=   (i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(UpperCAmelCase     ) / alpha_bar_fn(UpperCAmelCase     )							,      UpperCAmelCase     )     )
	return torch.tensor(UpperCAmelCase							,      dtype=torch.floataa     )



class 				__UpperCAmelCase	(							A__		,       A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     [e.name for e in KarrasDiffusionSchedulers]
		__lowerCAmelCase								=     1







		@register_to_config
		def __init__(self    :   Optional[int]   ,   _lowerCAmelCase    :   int = 1000   ,   _lowerCAmelCase    :   float = 0.0_001   ,   _lowerCAmelCase    :   float = 0.02   ,   _lowerCAmelCase    :   str = "linear"   ,   _lowerCAmelCase    :   Optional[Union[np.ndarray, List[float]]] = None   ,   _lowerCAmelCase    :   str = "epsilon"   ,   _lowerCAmelCase    :   str = "linear"   ,   _lowerCAmelCase    :   Optional[bool] = False   ,   _lowerCAmelCase    :   str = "linspace"   ,   _lowerCAmelCase    :   int = 0   ,   ):
			if trained_betas is not None:
				A					=   torch.tensor(_lowerCAmelCase   ,   dtype=torch.floataa   )
			elif beta_schedule == "linear":
				A					=   torch.linspace(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   ,   dtype=torch.floataa   )
			elif beta_schedule == "scaled_linear":
				# this schedule is very specific to the latent diffusion model.
				A					=   (
				    torch.linspace(beta_start**0.5   ,   beta_end**0.5   ,   _lowerCAmelCase   ,   dtype=torch.floataa   ) ** 2
				)
			elif beta_schedule == "squaredcos_cap_v2":
				# Glide cosine schedule
				A					=   betas_for_alpha_bar(_lowerCAmelCase   )
			else:
				raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""   )

			A					=   1.0 - self.betas
			A					=   torch.cumprod(self.alphas   ,   dim=0   )

			A					=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
			A					=   np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
			A					=   torch.from_numpy(_lowerCAmelCase   )

			# setable values
			A					=   None
			A					=   np.linspace(0   ,   num_train_timesteps - 1   ,   _lowerCAmelCase   ,   dtype=_lowerCAmelCase   )[::-1].copy()
			A					=   torch.from_numpy(_lowerCAmelCase   )
			A					=   False
			A					=   use_karras_sigmas







		@property
		def 							A  (self    :   Dict   ):
			# standard deviation of the initial noise distribution
			if self.config.timestep_spacing in ["linspace", "trailing"]:
				return self.sigmas.max()

			return (self.sigmas.max() ** 2 + 1) ** 0.5







		def 							A  (self    :   List[Any]   ,   _lowerCAmelCase    :   torch.FloatTensor   ,   _lowerCAmelCase    :   Union[float, torch.FloatTensor]   ):
			if isinstance(_lowerCAmelCase   ,   torch.Tensor   ):
				A					=   timestep.to(self.timesteps.device   )
			A					=   (self.timesteps == timestep).nonzero().item()
			A					=   self.sigmas[step_index]

			A					=   sample / ((sigma**2 + 1) ** 0.5)

			A					=   True
			return sample







		def 							A  (self    :   Tuple   ,   _lowerCAmelCase    :   int   ,   _lowerCAmelCase    :   Union[str, torch.device] = None   ):
			A					=   num_inference_steps

			# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
			if self.config.timestep_spacing == "linspace":
				A					=   np.linspace(0   ,   self.config.num_train_timesteps - 1   ,   _lowerCAmelCase   ,   dtype=_lowerCAmelCase   )[
				    ::-1
				].copy()
			elif self.config.timestep_spacing == "leading":
				A					=   self.config.num_train_timesteps // self.num_inference_steps
				# creates integer timesteps by multiplying by ratio
				# casting to int to avoid issues when num_inference_step is power of 3
				A					=   (np.arange(0   ,   _lowerCAmelCase   ) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase   )
				timesteps += self.config.steps_offset
			elif self.config.timestep_spacing == "trailing":
				A					=   self.config.num_train_timesteps / self.num_inference_steps
				# creates integer timesteps by multiplying by ratio
				# casting to int to avoid issues when num_inference_step is power of 3
				A					=   (np.arange(self.config.num_train_timesteps   ,   0   ,   -step_ratio   )).round().copy().astype(_lowerCAmelCase   )
				timesteps -= 1
			else:
				raise ValueError(
				    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""   )

			A					=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
			A					=   np.log(_lowerCAmelCase   )

			if self.config.interpolation_type == "linear":
				A					=   np.interp(_lowerCAmelCase   ,   np.arange(0   ,   len(_lowerCAmelCase   )   )   ,   _lowerCAmelCase   )
			elif self.config.interpolation_type == "log_linear":
				A					=   torch.linspace(np.log(sigmas[-1]   )   ,   np.log(sigmas[0]   )   ,   num_inference_steps + 1   ).exp()
			else:
				raise ValueError(
				    F"""{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"""
				    """ 'linear' or 'log_linear'"""   )

			if self.use_karras_sigmas:
				A					=   self._convert_to_karras(in_sigmas=_lowerCAmelCase   ,   num_inference_steps=self.num_inference_steps   )
				A					=   np.array([self._sigma_to_t(_lowerCAmelCase   ,   _lowerCAmelCase   ) for sigma in sigmas]   )

			A					=   np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
			A					=   torch.from_numpy(_lowerCAmelCase   ).to(device=_lowerCAmelCase   )
			if str(_lowerCAmelCase   ).startswith("""mps"""   ):
				# mps does not support float64
				A					=   torch.from_numpy(_lowerCAmelCase   ).to(_lowerCAmelCase   ,   dtype=torch.floataa   )
			else:
				A					=   torch.from_numpy(_lowerCAmelCase   ).to(device=_lowerCAmelCase   )







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   List[str]   ):
			# get log sigma
			A					=   np.log(_lowerCAmelCase   )

			# get distribution
			A					=   log_sigma - log_sigmas[:, np.newaxis]

			# get sigmas range
			A					=   np.cumsum((dists >= 0)   ,   axis=0   ).argmax(axis=0   ).clip(max=log_sigmas.shape[0] - 2   )
			A					=   low_idx + 1

			A					=   log_sigmas[low_idx]
			A					=   log_sigmas[high_idx]

			# interpolate sigmas
			A					=   (low - log_sigma) / (low - high)
			A					=   np.clip(_lowerCAmelCase   ,   0   ,   1   )

			# transform interpolation to time range
			A					=   (1 - w) * low_idx + w * high_idx
			A					=   t.reshape(sigma.shape   )
			return t







		def 							A  (self    :   Any   ,   _lowerCAmelCase    :   torch.FloatTensor   ,   _lowerCAmelCase    :   Tuple   ):
			A					=   in_sigmas[-1].item()
			A					=   in_sigmas[0].item()

			A					=   7.0  # 7.0 is the value used in the paper
			A					=   np.linspace(0   ,   1   ,   _lowerCAmelCase   )
			A					=   sigma_min ** (1 / rho)
			A					=   sigma_max ** (1 / rho)
			A					=   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
			return sigmas







		def 							A  (self    :   Tuple   ,   _lowerCAmelCase    :   torch.FloatTensor   ,   _lowerCAmelCase    :   Union[float, torch.FloatTensor]   ,   _lowerCAmelCase    :   torch.FloatTensor   ,   _lowerCAmelCase    :   float = 0.0   ,   _lowerCAmelCase    :   float = 0.0   ,   _lowerCAmelCase    :   float = float("""inf"""   )   ,   _lowerCAmelCase    :   float = 1.0   ,   _lowerCAmelCase    :   Optional[torch.Generator] = None   ,   _lowerCAmelCase    :   bool = True   ,   ):

			if (
			    isinstance(_lowerCAmelCase   ,   _lowerCAmelCase   )
			    or isinstance(_lowerCAmelCase   ,   torch.IntTensor   )
			    or isinstance(_lowerCAmelCase   ,   torch.LongTensor   )
			):
				raise ValueError(
				    (
				        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
				        """ `EulerDiscreteScheduler.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(_lowerCAmelCase   ,   torch.Tensor   ):
				A					=   timestep.to(self.timesteps.device   )

			A					=   (self.timesteps == timestep).nonzero().item()
			A					=   self.sigmas[step_index]

			A					=   min(s_churn / (len(self.sigmas   ) - 1)   ,   2**0.5 - 1   ) if s_tmin <= sigma <= s_tmax else 0.0

			A					=   randn_tensor(
			    model_output.shape   ,   dtype=model_output.dtype   ,   device=model_output.device   ,   generator=_lowerCAmelCase   )

			A					=   noise * s_noise
			A					=   sigma * (gamma + 1)

			if gamma > 0:
				A					=   sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

			# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
			# NOTE: "original_sample" should not be an expected prediction_type but is left in for
			# backwards compatibility
			if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
				A					=   model_output
			elif self.config.prediction_type == "epsilon":
				A					=   sample - sigma_hat * model_output
			elif self.config.prediction_type == "v_prediction":
				# * c_out + input * c_skip
				A					=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
			else:
				raise ValueError(
				    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""   )

			# 2. Convert to an ODE derivative
			A					=   (sample - pred_original_sample) / sigma_hat

			A					=   self.sigmas[step_index + 1] - sigma_hat

			A					=   sample + derivative * dt

			if not return_dict:
				return (prev_sample,)

			return EulerDiscreteSchedulerOutput(prev_sample=_lowerCAmelCase   ,   pred_original_sample=_lowerCAmelCase   )







		def 							A  (self    :   Dict   ,   _lowerCAmelCase    :   torch.FloatTensor   ,   _lowerCAmelCase    :   torch.FloatTensor   ,   _lowerCAmelCase    :   torch.FloatTensor   ,   ):
			# Make sure sigmas and timesteps have the same device and dtype as original_samples
			A					=   self.sigmas.to(device=original_samples.device   ,   dtype=original_samples.dtype   )
			if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase   ):
				# mps does not support float64
				A					=   self.timesteps.to(original_samples.device   ,   dtype=torch.floataa   )
				A					=   timesteps.to(original_samples.device   ,   dtype=torch.floataa   )
			else:
				A					=   self.timesteps.to(original_samples.device   )
				A					=   timesteps.to(original_samples.device   )

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

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

			A					=   original_samples + noise * sigma
			return noisy_samples







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








'''simple docstring'''


from math import pi




def  __a			(  UpperCAmelCase     )   ->float:



	"""simple docstring"""







	return degree / (180 / pi)


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

				testmod()

