





'''simple docstring'''







import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


snake_case_       :    List[Any]          =					10_00


class    __a :

							def __init__(							self							:       Optional[int]    ,       __magic_name__							:       Tuple    ,       __magic_name__							:       List[str]=1.2    ,       __magic_name__							:       str="g"    ,       __magic_name__							:       str=0.5    ,       __magic_name__							:       Optional[int]="-"    ,       __magic_name__							:       Optional[Any]="test_out.jpg"    ,       __magic_name__							:       Optional[int]=True    ,       __magic_name__							:       Any=False    ,       __magic_name__							:       Optional[Any]=None    ,       __magic_name__							:       int=None    ,       __magic_name__							:       List[Any]=0.7    ,       )   ->    int:







													"""simple docstring"""
													if isinstance(__magic_name__    ,       torch.Tensor				):
																			UpperCAmelCase_     : Tuple         =					img.numpy().astype('''np.uint8'''				)
													if isinstance(__magic_name__    ,       __magic_name__				):
																			UpperCAmelCase_     : Dict         =					img_tensorize(__magic_name__				)
													assert isinstance(__magic_name__    ,       np.ndarray				)

													UpperCAmelCase_  ,    UpperCAmelCase_     : Any         =					img.shape[1], img.shape[0]
													UpperCAmelCase_     : int         =					mplfigure.Figure(frameon=__magic_name__				)
													UpperCAmelCase_     : Tuple         =					fig.get_dpi()
													UpperCAmelCase_     : List[str]         =					(width * scale + 1E-2) / dpi
													UpperCAmelCase_     : List[str]         =					(height * scale + 1E-2) / dpi
													fig.set_size_inches(__magic_name__    ,       __magic_name__				)
													UpperCAmelCase_     : Optional[int]         =					fig.add_axes([0.0, 0.0, 1.0, 1.0]				)
													ax.axis('''off'''				)
													ax.set_xlim(0.0    ,       __magic_name__				)
													ax.set_ylim(__magic_name__				)

													UpperCAmelCase_     : str         =					saveas
													UpperCAmelCase_     : List[Any]         =					rgb
													UpperCAmelCase_     : List[str]         =					pynb
													UpperCAmelCase_     : List[str]         =					img
													UpperCAmelCase_     : Optional[int]         =					edgecolor
													UpperCAmelCase_     : Optional[int]         =					0.5
													UpperCAmelCase_     : List[Any]         =					linestyle
													UpperCAmelCase_     : Optional[int]         =					int(np.sqrt(min(__magic_name__    ,       __magic_name__				)				) * scale // 3				)
													UpperCAmelCase_     : Optional[Any]         =					width
													UpperCAmelCase_     : Dict         =					height
													UpperCAmelCase_     : Tuple         =					scale
													UpperCAmelCase_     : Tuple         =					fig
													UpperCAmelCase_     : Optional[int]         =					ax
													UpperCAmelCase_     : Optional[Any]         =					pad
													UpperCAmelCase_     : str         =					idaobj
													UpperCAmelCase_     : Any         =					idaattr
													UpperCAmelCase_     : str         =					FigureCanvasAgg(__magic_name__				)

							def       UpperCAmelCase__		(							self							:       str    ,       __magic_name__							:       int    ,       __magic_name__							:       Any=None				)   ->    Optional[int]:







													"""simple docstring"""
													if color is None:
																			UpperCAmelCase_     : Any         =					self.edgecolor
													((UpperCAmelCase_)  ,    (UpperCAmelCase_)  ,    (UpperCAmelCase_)  ,    (UpperCAmelCase_))     : List[str]         =					box
													UpperCAmelCase_     : Optional[int]         =					xa - xa
													UpperCAmelCase_     : str         =					ya - ya
													self.ax.add_patch(
													    mpl.patches.Rectangle(
													        (xa, ya)    ,       __magic_name__    ,       __magic_name__    ,       fill=__magic_name__    ,       edgecolor=__magic_name__    ,       linewidth=self.font_size // 3    ,       alpha=self.alpha    ,       linestyle=self.linestyle    ,       )				)

							def       UpperCAmelCase__		(							self							:       Optional[int]    ,       __magic_name__							:       List[str]    ,       __magic_name__							:       Union[str, Any]=None    ,       __magic_name__							:       str=None    ,       __magic_name__							:       Any=None    ,       __magic_name__							:       Tuple=None				)   ->    int:







													"""simple docstring"""
													if len(boxes.shape				) > 2:
																			UpperCAmelCase_     : List[str]         =					boxes[0]
													if len(obj_ids.shape				) > 1:
																			UpperCAmelCase_     : Any         =					obj_ids[0]
													if len(obj_scores.shape				) > 1:
																			UpperCAmelCase_     : Optional[int]         =					obj_scores[0]
													if len(attr_ids.shape				) > 1:
																			UpperCAmelCase_     : Any         =					attr_ids[0]
													if len(attr_scores.shape				) > 1:
																			UpperCAmelCase_     : List[Any]         =					attr_scores[0]
													if isinstance(__magic_name__    ,       torch.Tensor				):
																			UpperCAmelCase_     : int         =					boxes.numpy()
													if isinstance(__magic_name__    ,       __magic_name__				):
																			UpperCAmelCase_     : Union[str, Any]         =					np.array(__magic_name__				)
													assert isinstance(__magic_name__    ,       np.ndarray				)
													UpperCAmelCase_     : Union[str, Any]         =					np.prod(boxes[:, 2:] - boxes[:, :2]    ,       axis=1				)
													UpperCAmelCase_     : List[Any]         =					np.argsort(-areas				).tolist()
													UpperCAmelCase_     : str         =					boxes[sorted_idxs] if boxes is not None else None
													UpperCAmelCase_     : Any         =					obj_ids[sorted_idxs] if obj_ids is not None else None
													UpperCAmelCase_     : int         =					obj_scores[sorted_idxs] if obj_scores is not None else None
													UpperCAmelCase_     : List[Any]         =					attr_ids[sorted_idxs] if attr_ids is not None else None
													UpperCAmelCase_     : Dict         =					attr_scores[sorted_idxs] if attr_scores is not None else None

													UpperCAmelCase_     : Dict         =					[self._random_color(maximum=1				) for _ in range(len(__magic_name__				)				)]
													UpperCAmelCase_     : Optional[Any]         =					[assigned_colors[idx] for idx in sorted_idxs]
													if obj_ids is not None:
																			UpperCAmelCase_     : int         =					self._create_text_labels_attr(__magic_name__    ,       __magic_name__    ,       __magic_name__    ,       __magic_name__				)
																			for i in range(len(__magic_name__				)				):
																									UpperCAmelCase_     : str         =					assigned_colors[i]
																									self.add_box(boxes[i]    ,       __magic_name__				)
																									self.draw_labels(labels[i]    ,       boxes[i]    ,       __magic_name__				)

							def       UpperCAmelCase__		(							self							:       Any    ,       __magic_name__							:       Any    ,       __magic_name__							:       Dict    ,       __magic_name__							:       Dict				)   ->    Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_  ,    UpperCAmelCase_  ,    UpperCAmelCase_  ,    UpperCAmelCase_     : Optional[int]         =					box
													UpperCAmelCase_     : Optional[int]         =					(xa, ya)
													UpperCAmelCase_     : str         =					(ya - ya) * (xa - xa)
													UpperCAmelCase_     : List[Any]         =					_SMALL_OBJ * self.scale
													if instance_area < small or ya - ya < 40 * self.scale:
																			if ya >= self.height - 5:
																									UpperCAmelCase_     : Any         =					(xa, ya)
																			else:
																									UpperCAmelCase_     : Dict         =					(xa, ya)

													UpperCAmelCase_     : List[str]         =					(ya - ya) / np.sqrt(self.height * self.width				)
													UpperCAmelCase_     : List[str]         =					self._change_color_brightness(__magic_name__    ,       brightness_factor=0.7				)
													UpperCAmelCase_     : Optional[Any]         =					np.clip((height_ratio - 0.0_2) / 0.0_8 + 1    ,       1.2    ,       2				)
													font_size *= 0.7_5 * self.font_size

													self.draw_text(
													    text=__magic_name__    ,       position=__magic_name__    ,       color=__magic_name__    ,       )

							def       UpperCAmelCase__		(							self							:       List[str]    ,       __magic_name__							:       Optional[Any]    ,       __magic_name__							:       str    ,       __magic_name__							:       Union[str, Any]="g"    ,       __magic_name__							:       List[str]="left"    ,       )   ->    int:







													"""simple docstring"""
													UpperCAmelCase_     : Union[str, Any]         =					0
													UpperCAmelCase_     : Union[str, Any]         =					self.font_size
													UpperCAmelCase_     : List[str]         =					np.maximum(list(mplc.to_rgb(__magic_name__				)				)    ,       0.2				)
													UpperCAmelCase_     : List[Any]         =					max(0.8    ,       np.max(__magic_name__				)				)
													UpperCAmelCase_     : Optional[Any]         =					{
													    '''facecolor''': '''black''',
													    '''alpha''': self.alpha,
													    '''pad''': self.pad,
													    '''edgecolor''': '''none''',
													}
													UpperCAmelCase_  ,    UpperCAmelCase_     : str         =					position
													self.ax.text(
													    __magic_name__    ,       __magic_name__    ,       __magic_name__    ,       size=font_size * self.scale    ,       family='''sans-serif'''    ,       bbox=__magic_name__    ,       verticalalignment='''top'''    ,       horizontalalignment=__magic_name__    ,       color=__magic_name__    ,       zorder=10    ,       rotation=__magic_name__    ,       )

							def       UpperCAmelCase__		(							self							:       Optional[int]    ,       __magic_name__							:       Union[str, Any]=None				)   ->    List[Any]:







													"""simple docstring"""
													if saveas is None:
																			UpperCAmelCase_     : Union[str, Any]         =					self.saveas
													if saveas.lower().endswith('''.jpg'''				) or saveas.lower().endswith('''.png'''				):
																			cva.imwrite(
																			    __magic_name__    ,       self._get_buffer()[:, :, ::-1]    ,       )
													else:
																			self.fig.savefig(__magic_name__				)

							def       UpperCAmelCase__		(							self							:       str    ,       __magic_name__							:       Dict    ,       __magic_name__							:       List[Any]    ,       __magic_name__							:       List[str]    ,       __magic_name__							:       List[str]				)   ->    str:







													"""simple docstring"""
													UpperCAmelCase_     : Optional[int]         =					[self.idaobj[i] for i in classes]
													UpperCAmelCase_     : int         =					[self.idaattr[i] for i in attr_classes]
													UpperCAmelCase_     : str         =					[
													    F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
													    for label, score, attr, attr_score in zip(__magic_name__    ,       __magic_name__    ,       __magic_name__    ,       __magic_name__				)
													]
													return labels

							def       UpperCAmelCase__		(							self							:       Optional[Any]    ,       __magic_name__							:       Tuple    ,       __magic_name__							:       Optional[int]				)   ->    Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : Union[str, Any]         =					[self.idaobj[i] for i in classes]
													if scores is not None:
																			if labels is None:
																									UpperCAmelCase_     : str         =					['''{:.0f}%'''.format(s * 1_00				) for s in scores]
																			else:
																									UpperCAmelCase_     : Any         =					['''{} {:.0f}%'''.format(__magic_name__    ,       s * 1_00				) for li, s in zip(__magic_name__    ,       __magic_name__				)]
													return labels

							def       UpperCAmelCase__		(							self							:       List[str]    ,       __magic_name__							:       Tuple=2_55				)   ->    Any:







													"""simple docstring"""
													UpperCAmelCase_     : Tuple         =					np.random.randint(0    ,       len(_COLORS				)				)
													UpperCAmelCase_     : Optional[Any]         =					_COLORS[idx] * maximum
													if not self.rgb:
																			UpperCAmelCase_     : Optional[Any]         =					ret[::-1]
													return ret

							def       UpperCAmelCase__		(							self							:       Dict				)   ->    Tuple:







													"""simple docstring"""
													if not self.pynb:
																			UpperCAmelCase_  ,    UpperCAmelCase_     : List[str]         =					self.canvas.print_to_buffer()
																			if (width, height) != (self.width, self.height):
																									UpperCAmelCase_     : str         =					cva.resize(self.img    ,       (width, height)				)
																			else:
																									UpperCAmelCase_     : Optional[Any]         =					self.img
													else:
																			UpperCAmelCase_     : List[str]         =					io.BytesIO()  # works for cairo backend
																			self.canvas.print_rgba(__magic_name__				)
																			UpperCAmelCase_  ,    UpperCAmelCase_     : List[Any]         =					self.width, self.height
																			UpperCAmelCase_     : Optional[Any]         =					buf.getvalue()
																			UpperCAmelCase_     : int         =					self.img

													UpperCAmelCase_     : Any         =					np.frombuffer(__magic_name__    ,       dtype='''uint8'''				)
													UpperCAmelCase_     : Union[str, Any]         =					buffer.reshape(__magic_name__    ,       __magic_name__    ,       4				)
													UpperCAmelCase_  ,    UpperCAmelCase_     : Optional[Any]         =					np.split(__magic_name__    ,       [3]    ,       axis=2				)

													try:
																			import numexpr as ne  # fuse them with numexpr

																			UpperCAmelCase_     : List[str]         =					ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''				)
													except ImportError:
																			UpperCAmelCase_     : Optional[int]         =					alpha.astype('''float32'''				) / 2_5_5.0
																			UpperCAmelCase_     : List[str]         =					img * (1 - alpha) + rgb * alpha

													return visualized_image.astype('''uint8'''				)



							def       UpperCAmelCase__		(							self							:       Union[str, Any]    ,       __magic_name__							:       Optional[Any]    ,       __magic_name__							:       Dict				)   ->    List[str]:







													"""simple docstring"""
													assert brightness_factor >= -1.0 and brightness_factor <= 1.0
													UpperCAmelCase_     : Optional[Any]         =					mplc.to_rgb(__magic_name__				)
													UpperCAmelCase_     : str         =					colorsys.rgb_to_hls(*mplc.to_rgb(__magic_name__				)				)
													UpperCAmelCase_     : Dict         =					polygon_color[1] + (brightness_factor * polygon_color[1])
													UpperCAmelCase_     : List[Any]         =					0.0 if modified_lightness < 0.0 else modified_lightness
													UpperCAmelCase_     : Optional[Any]         =					1.0 if modified_lightness > 1.0 else modified_lightness
													UpperCAmelCase_     : List[str]         =					colorsys.hls_to_rgb(polygon_color[0]    ,       __magic_name__    ,       polygon_color[2]				)
													return modified_color





snake_case_       :    Any          =					(
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)












'''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


snake_case_       :    Union[str, Any]          =					logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class    __a (lowerCamelCase       ):
							__a   :				torch.FloatTensor
							__a   :				Optional[torch.FloatTensor]       =    None





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     str,     SCREAMING_SNAKE_CASE__ :     Optional[Any]=0.9_99,     SCREAMING_SNAKE_CASE__ :     Tuple="cosine",     )	->		Optional[Any]:
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(SCREAMING_SNAKE_CASE__ :     Union[str, Any]				):
																		return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2				) ** 2

						elif alpha_transform_type == "exp":

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

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

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


class    __a (lowerCamelCase    ,						lowerCamelCase       ):
							__a   :				str       =    [e.name for e in KarrasDiffusionSchedulers]
							__a   :				Tuple       =    1

							@register_to_config
							def __init__(							self							:       Union[str, Any]    ,       __magic_name__							:       int = 10_00    ,       __magic_name__							:       float = 0.0_0_0_1    ,       __magic_name__							:       float = 0.0_2    ,       __magic_name__							:       str = "linear"    ,       __magic_name__							:       Optional[Union[np.ndarray, List[float]]] = None    ,       __magic_name__							:       str = "epsilon"    ,       __magic_name__							:       str = "linear"    ,       __magic_name__							:       Optional[bool] = False    ,       __magic_name__							:       str = "linspace"    ,       __magic_name__							:       int = 0    ,       )   ->    Any:







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

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

													UpperCAmelCase_     : Dict         =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
													UpperCAmelCase_     : Optional[Any]         =					np.concatenate([sigmas[::-1], [0.0]]				).astype(np.floataa				)
													UpperCAmelCase_     : Optional[Any]         =					torch.from_numpy(__magic_name__				)

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

							@property
							def       UpperCAmelCase__		(							self							:       Dict				)   ->    List[Any]:







													"""simple docstring"""
													# 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       UpperCAmelCase__		(							self							:       Tuple    ,       __magic_name__							:       torch.FloatTensor    ,       __magic_name__							:       Union[float, torch.FloatTensor]				)   ->    torch.FloatTensor:







													"""simple docstring"""
													if isinstance(__magic_name__    ,       torch.Tensor				):
																			UpperCAmelCase_     : Tuple         =					timestep.to(self.timesteps.device				)
													UpperCAmelCase_     : Tuple         =					(self.timesteps == timestep).nonzero().item()
													UpperCAmelCase_     : List[str]         =					self.sigmas[step_index]

													UpperCAmelCase_     : List[str]         =					sample / ((sigma**2 + 1) ** 0.5)

													UpperCAmelCase_     : Tuple         =					True
													return sample

							def       UpperCAmelCase__		(							self							:       Optional[int]    ,       __magic_name__							:       int    ,       __magic_name__							:       Union[str, torch.device] = None				)   ->    Any:







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

													if self.config.interpolation_type == "linear":
																			UpperCAmelCase_     : List[Any]         =					np.interp(__magic_name__    ,       np.arange(0    ,       len(__magic_name__				)				)    ,       __magic_name__				)
													elif self.config.interpolation_type == "log_linear":
																			UpperCAmelCase_     : 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:
																			UpperCAmelCase_     : List[Any]         =					self._convert_to_karras(in_sigmas=__magic_name__    ,       num_inference_steps=self.num_inference_steps				)
																			UpperCAmelCase_     : Dict         =					np.array([self._sigma_to_t(__magic_name__    ,       __magic_name__				) for sigma in sigmas]				)

													UpperCAmelCase_     : Tuple         =					np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
													UpperCAmelCase_     : Any         =					torch.from_numpy(__magic_name__				).to(device=__magic_name__				)
													if str(__magic_name__				).startswith('''mps'''				):
																			# mps does not support float64
																			UpperCAmelCase_     : List[str]         =					torch.from_numpy(__magic_name__				).to(__magic_name__    ,       dtype=torch.floataa				)
													else:
																			UpperCAmelCase_     : int         =					torch.from_numpy(__magic_name__				).to(device=__magic_name__				)

							def       UpperCAmelCase__		(							self							:       Union[str, Any]    ,       __magic_name__							:       Union[str, Any]    ,       __magic_name__							:       Optional[Any]				)   ->    Optional[Any]:







													"""simple docstring"""
													# get log sigma
													UpperCAmelCase_     : List[Any]         =					np.log(__magic_name__				)

													# get distribution
													UpperCAmelCase_     : Optional[int]         =					log_sigma - log_sigmas[:, np.newaxis]

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

													UpperCAmelCase_     : Optional[int]         =					log_sigmas[low_idx]
													UpperCAmelCase_     : Union[str, Any]         =					log_sigmas[high_idx]

													# interpolate sigmas
													UpperCAmelCase_     : Union[str, Any]         =					(low - log_sigma) / (low - high)
													UpperCAmelCase_     : Dict         =					np.clip(__magic_name__    ,       0    ,       1				)

													# transform interpolation to time range
													UpperCAmelCase_     : Optional[Any]         =					(1 - w) * low_idx + w * high_idx
													UpperCAmelCase_     : List[Any]         =					t.reshape(sigma.shape				)
													return t

							def       UpperCAmelCase__		(							self							:       str    ,       __magic_name__							:       torch.FloatTensor    ,       __magic_name__							:       Union[str, Any]				)   ->    torch.FloatTensor:







													"""simple docstring"""
													UpperCAmelCase_     : float         =					in_sigmas[-1].item()
													UpperCAmelCase_     : float         =					in_sigmas[0].item()

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

							def       UpperCAmelCase__		(							self							:       List[Any]    ,       __magic_name__							:       torch.FloatTensor    ,       __magic_name__							:       Union[float, torch.FloatTensor]    ,       __magic_name__							:       torch.FloatTensor    ,       __magic_name__							:       float = 0.0    ,       __magic_name__							:       float = 0.0    ,       __magic_name__							:       float = float('''inf'''				)    ,       __magic_name__							:       float = 1.0    ,       __magic_name__							:       Optional[torch.Generator] = None    ,       __magic_name__							:       bool = True    ,       )   ->    Union[EulerDiscreteSchedulerOutput, Tuple]:







													"""simple docstring"""

													if (
													    isinstance(__magic_name__    ,       __magic_name__				)
													    or isinstance(__magic_name__    ,       torch.IntTensor				)
													    or isinstance(__magic_name__    ,       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(__magic_name__    ,       torch.Tensor				):
																			UpperCAmelCase_     : Tuple         =					timestep.to(self.timesteps.device				)

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

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

													UpperCAmelCase_     : Dict         =					randn_tensor(
													    model_output.shape    ,       dtype=model_output.dtype    ,       device=model_output.device    ,       generator=__magic_name__				)

													UpperCAmelCase_     : Tuple         =					noise * s_noise
													UpperCAmelCase_     : Tuple         =					sigma * (gamma + 1)

													if gamma > 0:
																			UpperCAmelCase_     : Any         =					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":
																			UpperCAmelCase_     : str         =					model_output
													elif self.config.prediction_type == "epsilon":
																			UpperCAmelCase_     : Any         =					sample - sigma_hat * model_output
													elif self.config.prediction_type == "v_prediction":
																			# * c_out + input * c_skip
																			UpperCAmelCase_     : Optional[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
													UpperCAmelCase_     : int         =					(sample - pred_original_sample) / sigma_hat

													UpperCAmelCase_     : List[str]         =					self.sigmas[step_index + 1] - sigma_hat

													UpperCAmelCase_     : str         =					sample + derivative * dt

													if not return_dict:
																			return (prev_sample,)

													return EulerDiscreteSchedulerOutput(prev_sample=__magic_name__    ,       pred_original_sample=__magic_name__				)

							def       UpperCAmelCase__		(							self							:       Optional[int]    ,       __magic_name__							:       torch.FloatTensor    ,       __magic_name__							:       torch.FloatTensor    ,       __magic_name__							:       torch.FloatTensor    ,       )   ->    torch.FloatTensor:







													"""simple docstring"""
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													UpperCAmelCase_     : Union[str, Any]         =					self.sigmas.to(device=original_samples.device    ,       dtype=original_samples.dtype				)
													if original_samples.device.type == "mps" and torch.is_floating_point(__magic_name__				):
																			# mps does not support float64
																			UpperCAmelCase_     : str         =					self.timesteps.to(original_samples.device    ,       dtype=torch.floataa				)
																			UpperCAmelCase_     : Dict         =					timesteps.to(original_samples.device    ,       dtype=torch.floataa				)
													else:
																			UpperCAmelCase_     : Optional[int]         =					self.timesteps.to(original_samples.device				)
																			UpperCAmelCase_     : Dict         =					timesteps.to(original_samples.device				)

													UpperCAmelCase_     : List[str]         =					[(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_     : str         =					original_samples + noise * sigma
													return noisy_samples



							def __len__(							self							:       Optional[Any]				)   ->    int:







													"""simple docstring"""
													return self.config.num_train_timesteps





