

"""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__				:  Optional[Any]													=  logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class     _UpperCAmelCase			(		__a):
			__a :       torch.FloatTensor
			__a :       Optional[torch.FloatTensor]									=							None
def       UpperCamelCase					(			_lowerCAmelCase     :						Any,  _lowerCAmelCase     :						str=0.999,  _lowerCAmelCase     :						Optional[int]="cosine",  )				->			List[Any]:
	if alpha_transform_type == "cosine":

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

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(_lowerCAmelCase     :						Optional[Any] ):
			return math.exp(t * -12.0 )

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

	_UpperCAmelCase    :							Tuple									=					[]
	for i in range(_lowerCAmelCase ):
		_UpperCAmelCase    :							Dict									=					i / num_diffusion_timesteps
		_UpperCAmelCase    :							int									=					(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(_lowerCAmelCase ) / alpha_bar_fn(_lowerCAmelCase ),  _lowerCAmelCase ) )
	return torch.tensor(_lowerCAmelCase,  dtype=torch.floataa )






class     _UpperCAmelCase			(		__a    ,     __a):
			__a :       List[str]									=							[e.name for e in KarrasDiffusionSchedulers]
			__a :       Optional[int]									=							1
			@register_to_config
			def __init__(		self      ,		_A = 10_00      ,		_A = 0.0001      ,		_A = 0.02      ,		_A = "linear"      ,		_A = None      ,		_A = "epsilon"      ,		_A = "linspace"      ,		_A = 0      ,		)    ->    Any:






				'''simple docstring'''







				if trained_betas is not None:
					_UpperCAmelCase    :							List[str]									=					torch.tensor(_A      ,		dtype=torch.floataa     )
				elif beta_schedule == "linear":
					_UpperCAmelCase    :							str									=					torch.linspace(_A      ,		_A      ,		_A      ,		dtype=torch.floataa     )
				elif beta_schedule == "scaled_linear":
					# this schedule is very specific to the latent diffusion model.
					_UpperCAmelCase    :							int									=					(
					    torch.linspace(beta_start**0.5      ,		beta_end**0.5      ,		_A      ,		dtype=torch.floataa     ) ** 2
					)
				elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					_UpperCAmelCase    :							List[Any]									=					betas_for_alpha_bar(_A     )
				else:
					raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''     )

				_UpperCAmelCase    :							Union[str, Any]									=					1.0 - self.betas
				_UpperCAmelCase    :							Tuple									=					torch.cumprod(self.alphas      ,		dim=0     )

				_UpperCAmelCase    :							Optional[Any]									=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
				_UpperCAmelCase    :							List[str]									=					np.concatenate([sigmas[::-1], [0.0]]     ).astype(np.floataa     )
				_UpperCAmelCase    :							Any									=					torch.from_numpy(_A     )

				# setable values
				_UpperCAmelCase    :							Optional[int]									=					None
				_UpperCAmelCase    :							Union[str, Any]									=					np.linspace(0      ,		num_train_timesteps - 1      ,		_A      ,		dtype=_A     )[::-1].copy()
				_UpperCAmelCase    :							str									=					torch.from_numpy(_A     )
				_UpperCAmelCase    :							Optional[Any]									=					False
			@property
			def 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				if self.config.timestep_spacing in ["linspace", "trailing"]:
					return self.sigmas.max()

				return (self.sigmas.max() ** 2 + 1) ** 0.5
			def 							__snake_case (		self      ,		_A      ,		_A     )    ->    torch.FloatTensor:






				'''simple docstring'''







				if isinstance(_A      ,		torch.Tensor     ):
					_UpperCAmelCase    :							Tuple									=					timestep.to(self.timesteps.device     )
				_UpperCAmelCase    :							str									=					(self.timesteps == timestep).nonzero().item()
				_UpperCAmelCase    :							Union[str, Any]									=					self.sigmas[step_index]
				_UpperCAmelCase    :							Union[str, Any]									=					sample / ((sigma**2 + 1) ** 0.5)
				_UpperCAmelCase    :							List[Any]									=					True
				return sample
			def 							__snake_case (		self      ,		_A      ,		_A = None     )    ->    Any:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					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":
					_UpperCAmelCase    :							Dict									=					np.linspace(0      ,		self.config.num_train_timesteps - 1      ,		_A      ,		dtype=_A     )[
					    ::-1
					].copy()
				elif self.config.timestep_spacing == "leading":
					_UpperCAmelCase    :							Dict									=					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
					_UpperCAmelCase    :							Union[str, Any]									=					(np.arange(0      ,		_A     ) * step_ratio).round()[::-1].copy().astype(_A     )
					timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
					_UpperCAmelCase    :							List[Any]									=					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
					_UpperCAmelCase    :							Any									=					(np.arange(self.config.num_train_timesteps      ,		0      ,		-step_ratio     )).round().copy().astype(_A     )
					timesteps -= 1
				else:
					raise ValueError(
					    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''     )

				_UpperCAmelCase    :							int									=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
				_UpperCAmelCase    :							Optional[int]									=					np.interp(_A      ,		np.arange(0      ,		len(_A     )     )      ,		_A     )
				_UpperCAmelCase    :							str									=					np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
				_UpperCAmelCase    :							Dict									=					torch.from_numpy(_A     ).to(device=_A     )
				if str(_A     ).startswith("""mps"""     ):
					# mps does not support float64
					_UpperCAmelCase    :							List[str]									=					torch.from_numpy(_A     ).to(_A      ,		dtype=torch.floataa     )
				else:
					_UpperCAmelCase    :							Union[str, Any]									=					torch.from_numpy(_A     ).to(device=_A     )
			def 							__snake_case (		self      ,		_A      ,		_A      ,		_A      ,		_A = None      ,		_A = True      ,		)    ->    Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:






				'''simple docstring'''







				if (
				    isinstance(_A      ,		_A     )
				    or isinstance(_A      ,		torch.IntTensor     )
				    or isinstance(_A      ,		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(_A      ,		torch.Tensor     ):
					_UpperCAmelCase    :							List[Any]									=					timestep.to(self.timesteps.device     )

				_UpperCAmelCase    :							int									=					(self.timesteps == timestep).nonzero().item()
				_UpperCAmelCase    :							Optional[Any]									=					self.sigmas[step_index]

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				if self.config.prediction_type == "epsilon":
					_UpperCAmelCase    :							str									=					sample - sigma * model_output
				elif self.config.prediction_type == "v_prediction":
					# * c_out + input * c_skip
					_UpperCAmelCase    :							List[Any]									=					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
				elif self.config.prediction_type == "sample":
					raise NotImplementedError("""prediction_type not implemented yet: sample"""     )
				else:
					raise ValueError(
					    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''     )

				_UpperCAmelCase    :							Optional[int]									=					self.sigmas[step_index]
				_UpperCAmelCase    :							str									=					self.sigmas[step_index + 1]
				_UpperCAmelCase    :							List[Any]									=					(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
				_UpperCAmelCase    :							List[Any]									=					(sigma_to**2 - sigma_up**2) ** 0.5

				# 2. Convert to an ODE derivative
				_UpperCAmelCase    :							Optional[int]									=					(sample - pred_original_sample) / sigma

				_UpperCAmelCase    :							Optional[Any]									=					sigma_down - sigma

				_UpperCAmelCase    :							Dict									=					sample + derivative * dt

				_UpperCAmelCase    :							int									=					model_output.device
				_UpperCAmelCase    :							Any									=					randn_tensor(model_output.shape      ,		dtype=model_output.dtype      ,		device=_A      ,		generator=_A     )

				_UpperCAmelCase    :							Any									=					prev_sample + noise * sigma_up

				if not return_dict:
					return (prev_sample,)

				return EulerAncestralDiscreteSchedulerOutput(
				    prev_sample=_A      ,		pred_original_sample=_A     )
			def 							__snake_case (		self      ,		_A      ,		_A      ,		_A      ,		)    ->    torch.FloatTensor:






				'''simple docstring'''







				_UpperCAmelCase    :							Tuple									=					self.sigmas.to(device=original_samples.device      ,		dtype=original_samples.dtype     )
				if original_samples.device.type == "mps" and torch.is_floating_point(_A     ):
					# mps does not support float64
					_UpperCAmelCase    :							str									=					self.timesteps.to(original_samples.device      ,		dtype=torch.floataa     )
					_UpperCAmelCase    :							Union[str, Any]									=					timesteps.to(original_samples.device      ,		dtype=torch.floataa     )
				else:
					_UpperCAmelCase    :							Any									=					self.timesteps.to(original_samples.device     )
					_UpperCAmelCase    :							Any									=					timesteps.to(original_samples.device     )

				_UpperCAmelCase    :							Dict									=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

				_UpperCAmelCase    :							Any									=					original_samples + noise * sigma
				return noisy_samples
			def __len__(		self     )    ->    List[Any]:






				'''simple docstring'''







				return self.config.num_train_timesteps








"""simple docstring"""






def       UpperCamelCase					(			_lowerCAmelCase     :						int,  _lowerCAmelCase     :						int = 1000 )				->			str:
	if (not isinstance(_lowerCAmelCase,  _lowerCAmelCase )) or (digit_position <= 0):
		raise ValueError("""Digit position must be a positive integer""" )
	elif (not isinstance(_lowerCAmelCase,  _lowerCAmelCase )) or (precision < 0):
		raise ValueError("""Precision must be a nonnegative integer""" )

	# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
	# accurate
	_UpperCAmelCase    :							List[Any]									=					(
	    4 * _subsum(_lowerCAmelCase,  1,  _lowerCAmelCase )
	    - 2 * _subsum(_lowerCAmelCase,  4,  _lowerCAmelCase )
	    - _subsum(_lowerCAmelCase,  5,  _lowerCAmelCase )
	    - _subsum(_lowerCAmelCase,  6,  _lowerCAmelCase )
	)

	# return the first hex digit of the fractional part of the result
	return hex(int((sum_result % 1) * 16 ) )[2:]
def       UpperCamelCase					(			_lowerCAmelCase     :						int,  _lowerCAmelCase     :						int,  _lowerCAmelCase     :						int )				->			float:
	_UpperCAmelCase    :							Optional[int]									=					0.0
	for sum_index in range(digit_pos_to_extract + precision ):
		_UpperCAmelCase    :							Optional[Any]									=					8 * sum_index + denominator_addend
		if sum_index < digit_pos_to_extract:
			# if the exponential term is an integer and we mod it by the denominator
			# before dividing, only the integer part of the sum will change;
			# the fractional part will not
			_UpperCAmelCase    :							Optional[Any]									=					pow(
			    16,  digit_pos_to_extract - 1 - sum_index,  _lowerCAmelCase )
		else:
			_UpperCAmelCase    :							int									=					pow(16,  digit_pos_to_extract - 1 - sum_index )
		total += exponential_term / denominator
	return total


if __name__ == "__main__":
						import doctest

						doctest.testmod()





