"""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


_UpperCamelCase         =		logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	torch.FloatTensor
			_UpperCamelCase     :	Optional[torch.FloatTensor]				    =							None





def    _A(		lowerCAmelCase ,     lowerCAmelCase=0.999 ,     lowerCAmelCase="cosine" ,     ):
							if alpha_transform_type == "cosine":

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

							elif alpha_transform_type == "exp":

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

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

							A__					:    Tuple														= []
							for i in range(lowerCAmelCase       ):
														A__					:    Any														= i / num_diffusion_timesteps
														A__					:    Optional[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    ):







			'''simple docstring'''
			_UpperCamelCase     :	Optional[int]				    =							[e.name for e in KarrasDiffusionSchedulers]
			_UpperCamelCase     :	Optional[Any]				    =							1





			@register_to_config
			def __init__( self      ,			snake_case_ = 1_000      ,			snake_case_ = 0.00_01      ,			snake_case_ = 0.02      ,			snake_case_ = "linear"      ,			snake_case_ = None      ,			snake_case_ = "epsilon"      ,			snake_case_ = "linear"      ,			snake_case_ = False      ,			snake_case_ = "linspace"      ,			snake_case_ = 0      ,			):







										'''simple docstring'''


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

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

										A__					:    Tuple														= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
										A__					:    List[str]														= np.concatenate([sigmas[::-1], [0.0]]       ).astype(np.floataa       )
										A__					:    List[str]														= torch.from_numpy(snake_case_       )

										# setable values
										A__					:    Optional[Any]														= None
										A__					:    Optional[int]														= np.linspace(0      ,			num_train_timesteps - 1      ,			snake_case_      ,			dtype=snake_case_       )[::-1].copy()
										A__					:    str														= torch.from_numpy(snake_case_       )
										A__					:    Dict														= False
										A__					:    int														= use_karras_sigmas





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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

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





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										if isinstance(snake_case_      ,			torch.Tensor       ):
																	A__					:    Tuple														= timestep.to(self.timesteps.device       )
										A__					:    int														= (self.timesteps == timestep).nonzero().item()
										A__					:    str														= self.sigmas[step_index]

										A__					:    List[Any]														= sample / ((sigma**2 + 1) ** 0.5)

										A__					:    Union[str, Any]														= True
										return sample





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_ = None       ):







										'''simple docstring'''


										A__					:    Optional[Any]														= 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__					:    Any														= np.linspace(0      ,			self.config.num_train_timesteps - 1      ,			snake_case_      ,			dtype=snake_case_       )[
																	    ::-1
																	].copy()
										elif self.config.timestep_spacing == "leading":
																	A__					:    List[str]														= 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__					:    Optional[int]														= (np.arange(0      ,			snake_case_       ) * step_ratio).round()[::-1].copy().astype(snake_case_       )
																	timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
																	A__					:    Union[str, 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
																	A__					:    Any														= (np.arange(self.config.num_train_timesteps      ,			0      ,			-step_ratio       )).round().copy().astype(snake_case_       )
																	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__					:    str														= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
										A__					:    Optional[int]														= np.log(snake_case_       )

										if self.config.interpolation_type == "linear":
																	A__					:    Optional[Any]														= np.interp(snake_case_      ,			np.arange(0      ,			len(snake_case_       )       )      ,			snake_case_       )
										elif self.config.interpolation_type == "log_linear":
																	A__					:    str														= 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__					:    Optional[Any]														= self._convert_to_karras(in_sigmas=snake_case_      ,			num_inference_steps=self.num_inference_steps       )
																	A__					:    Any														= np.array([self._sigma_to_t(snake_case_      ,			snake_case_       ) for sigma in sigmas]       )

										A__					:    int														= np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
										A__					:    Union[str, Any]														= torch.from_numpy(snake_case_       ).to(device=snake_case_       )
										if str(snake_case_       ).startswith("""mps"""       ):
																	# mps does not support float64
																	A__					:    int														= torch.from_numpy(snake_case_       ).to(snake_case_      ,			dtype=torch.floataa       )
										else:
																	A__					:    Dict														= torch.from_numpy(snake_case_       ).to(device=snake_case_       )





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Optional[Any]														= np.log(snake_case_       )

										# get distribution
										A__					:    List[Any]														= log_sigma - log_sigmas[:, np.newaxis]

										# get sigmas range
										A__					:    List[str]														= np.cumsum((dists >= 0)      ,			axis=0       ).argmax(axis=0       ).clip(max=log_sigmas.shape[0] - 2       )
										A__					:    Union[str, Any]														= low_idx + 1

										A__					:    Optional[int]														= log_sigmas[low_idx]
										A__					:    Optional[int]														= log_sigmas[high_idx]

										# interpolate sigmas
										A__					:    List[Any]														= (low - log_sigma) / (low - high)
										A__					:    Optional[Any]														= np.clip(snake_case_      ,			0      ,			1       )

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





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    float														= in_sigmas[-1].item()
										A__					:    float														= in_sigmas[0].item()

										A__					:    Union[str, Any]														= 7.0  # 7.0 is the value used in the paper
										A__					:    int														= np.linspace(0      ,			1      ,			snake_case_       )
										A__					:    Optional[Any]														= sigma_min ** (1 / rho)
										A__					:    Dict														= sigma_max ** (1 / rho)
										A__					:    Dict														= (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_ = 0.0      ,			snake_case_ = 0.0      ,			snake_case_ = float("""inf"""       )      ,			snake_case_ = 1.0      ,			snake_case_ = None      ,			snake_case_ = 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"""
																	        """ `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(snake_case_      ,			torch.Tensor       ):
																	A__					:    Optional[int]														= timestep.to(self.timesteps.device       )

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

										A__					:    Optional[Any]														= min(s_churn / (len(self.sigmas       ) - 1)      ,			2**0.5 - 1       ) if s_tmin <= sigma <= s_tmax else 0.0

										A__					:    List[str]														= randn_tensor(
										    model_output.shape      ,			dtype=model_output.dtype      ,			device=model_output.device      ,			generator=snake_case_       )

										A__					:    Optional[int]														= noise * s_noise
										A__					:    str														= sigma * (gamma + 1)

										if gamma > 0:
																	A__					:    Tuple														= 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__					:    Union[str, Any]														= model_output
										elif self.config.prediction_type == "epsilon":
																	A__					:    Dict														= sample - sigma_hat * model_output
										elif self.config.prediction_type == "v_prediction":
																	# * c_out + input * c_skip
																	A__					:    List[Any]														= 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__					:    Optional[Any]														= (sample - pred_original_sample) / sigma_hat

										A__					:    Tuple														= self.sigmas[step_index + 1] - sigma_hat

										A__					:    Optional[Any]														= sample + derivative * dt

										if not return_dict:
																	return (prev_sample,)

										return EulerDiscreteSchedulerOutput(prev_sample=snake_case_      ,			pred_original_sample=snake_case_       )





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			):







										'''simple docstring'''


										A__					:    Optional[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
																	A__					:    Tuple														= self.timesteps.to(original_samples.device      ,			dtype=torch.floataa       )
																	A__					:    Dict														= timesteps.to(original_samples.device      ,			dtype=torch.floataa       )
										else:
																	A__					:    Optional[int]														= self.timesteps.to(original_samples.device       )
																	A__					:    List[str]														= timesteps.to(original_samples.device       )

										A__					:    str														= [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

										A__					:    Dict														= original_samples + noise * sigma
										return noisy_samples





			def __len__( self       ):







										'''simple docstring'''


										return self.config.num_train_timesteps






"""simple docstring"""



from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_UpperCamelCase         =		TypeVar("T", bound=bool)
class 	__UpperCAmelCase						(Generic[T]    ):







			'''simple docstring'''





			def __init__( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    T														= value
										A__					:    RandomizedHeapNode[T] | None														= None
										A__					:    RandomizedHeapNode[T] | None														= None





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return self._value





			@staticmethod
			def 		lowerCamelCase				( snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										if not roota:
																	return roota

										if not roota:
																	return roota

										if roota.value > roota.value:
																	A__							,       A__					:    Union[str, Any]														= roota, roota

										if random.choice([True, False]       ):
																	A__							,       A__					:    Any														= roota.right, roota.left

										A__					:    Dict														= RandomizedHeapNode.merge(roota.left      ,			snake_case_       )

										return roota





class 	__UpperCAmelCase						(Generic[T]    ):







			'''simple docstring'''





			def __init__( self      ,			snake_case_ = ()       ):







										'''simple docstring'''


										A__					:    RandomizedHeapNode[T] | None														= None

										if data:
																	for item in data:
																								self.insert(snake_case_       )





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Dict														= RandomizedHeapNode.merge(self._root      ,			RandomizedHeapNode(snake_case_       )       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[str]														= self.top()

										if self._root is None:
																	return None

										A__					:    str														= RandomizedHeapNode.merge(self._root.left      ,			self._root.right       )

										return result





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										if not self._root:
																	raise IndexError("""Can't get top element for the empty heap."""       )
										return self._root.value





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[Any]														= None





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    int														= []
										while self:
																	result.append(self.pop()       )

										return result





			def __bool__( self       ):







										'''simple docstring'''


										return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





