


"""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


a_											=     10_00
class snake_case   :







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





						'''simple docstring'''







						if isinstance(a__     , torch.Tensor		):
										_A       	=      img.numpy().astype("np.uint8"		)
						if isinstance(a__     , a__		):
										_A       	=      img_tensorize(a__		)
						assert isinstance(a__     , np.ndarray		)

						_A	,		_A       	=      img.shape[1], img.shape[0]
						_A       	=      mplfigure.Figure(frameon=a__		)
						_A       	=      fig.get_dpi()
						_A       	=      (width * scale + 1E-2) / dpi
						_A       	=      (height * scale + 1E-2) / dpi
						fig.set_size_inches(a__     , a__		)
						_A       	=      fig.add_axes([0.0, 0.0, 1.0, 1.0]		)
						ax.axis("off"		)
						ax.set_xlim(0.0     , a__		)
						ax.set_ylim(a__		)

						_A       	=      saveas
						_A       	=      rgb
						_A       	=      pynb
						_A       	=      img
						_A       	=      edgecolor
						_A       	=      0.5
						_A       	=      linestyle
						_A       	=      int(np.sqrt(min(a__     , a__		)		) * scale // 3		)
						_A       	=      width
						_A       	=      height
						_A       	=      scale
						_A       	=      fig
						_A       	=      ax
						_A       	=      pad
						_A       	=      idaobj
						_A       	=      idaattr
						_A       	=      FigureCanvasAgg(a__		)







		def 			a_			(       self   :							List[Any]     , a__   :							Union[str, Any]     , a__   :							Tuple=None		)							->    Tuple:





						'''simple docstring'''







						if color is None:
										_A       	=      self.edgecolor
						((_A)	,		(_A)	,		(_A)	,		(_A))       	=      box
						_A       	=      xa - xa
						_A       	=      ya - ya
						self.ax.add_patch(
						    mpl.patches.Rectangle(
						        (xa, ya)     , a__     , a__     , fill=a__     , edgecolor=a__     , linewidth=self.font_size // 3     , alpha=self.alpha     , linestyle=self.linestyle     , )		)







		def 			a_			(       self   :							Union[str, Any]     , a__   :							Dict     , a__   :							List[Any]=None     , a__   :							int=None     , a__   :							str=None     , a__   :							Optional[int]=None		)							->    List[str]:





						'''simple docstring'''







						if len(boxes.shape		) > 2:
										_A       	=      boxes[0]
						if len(obj_ids.shape		) > 1:
										_A       	=      obj_ids[0]
						if len(obj_scores.shape		) > 1:
										_A       	=      obj_scores[0]
						if len(attr_ids.shape		) > 1:
										_A       	=      attr_ids[0]
						if len(attr_scores.shape		) > 1:
										_A       	=      attr_scores[0]
						if isinstance(a__     , torch.Tensor		):
										_A       	=      boxes.numpy()
						if isinstance(a__     , a__		):
										_A       	=      np.array(a__		)
						assert isinstance(a__     , np.ndarray		)
						_A       	=      np.prod(boxes[:, 2:] - boxes[:, :2]     , axis=1		)
						_A       	=      np.argsort(-areas		).tolist()
						_A       	=      boxes[sorted_idxs] if boxes is not None else None
						_A       	=      obj_ids[sorted_idxs] if obj_ids is not None else None
						_A       	=      obj_scores[sorted_idxs] if obj_scores is not None else None
						_A       	=      attr_ids[sorted_idxs] if attr_ids is not None else None
						_A       	=      attr_scores[sorted_idxs] if attr_scores is not None else None

						_A       	=      [self._random_color(maximum=1		) for _ in range(len(a__		)		)]
						_A       	=      [assigned_colors[idx] for idx in sorted_idxs]
						if obj_ids is not None:
										_A       	=      self._create_text_labels_attr(a__     , a__     , a__     , a__		)
										for i in range(len(a__		)		):
														_A       	=      assigned_colors[i]
														self.add_box(boxes[i]     , a__		)
														self.draw_labels(labels[i]     , boxes[i]     , a__		)







		def 			a_			(       self   :							List[str]     , a__   :							Any     , a__   :							List[str]     , a__   :							Tuple		)							->    int:





						'''simple docstring'''







						_A	,		_A	,		_A	,		_A       	=      box
						_A       	=      (xa, ya)
						_A       	=      (ya - ya) * (xa - xa)
						_A       	=      _SMALL_OBJ * self.scale
						if instance_area < small or ya - ya < 40 * self.scale:
										if ya >= self.height - 5:
														_A       	=      (xa, ya)
										else:
														_A       	=      (xa, ya)

						_A       	=      (ya - ya) / np.sqrt(self.height * self.width		)
						_A       	=      self._change_color_brightness(a__     , brightness_factor=0.7		)
						_A       	=      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=a__     , position=a__     , color=a__     , )







		def 			a_			(       self   :							str     , a__   :							Optional[int]     , a__   :							Optional[int]     , a__   :							Any="g"     , a__   :							Optional[int]="left"     , )							->    Dict:





						'''simple docstring'''







						_A       	=      0
						_A       	=      self.font_size
						_A       	=      np.maximum(list(mplc.to_rgb(a__		)		)     , 0.2		)
						_A       	=      max(0.8     , np.max(a__		)		)
						_A       	=      {
						    "facecolor": "black",
						    "alpha": self.alpha,
						    "pad": self.pad,
						    "edgecolor": "none",
						}
						_A	,		_A       	=      position
						self.ax.text(
						    a__     , a__     , a__     , size=font_size * self.scale     , family="sans-serif"     , bbox=a__     , verticalalignment="top"     , horizontalalignment=a__     , color=a__     , zorder=10     , rotation=a__     , )







		def 			a_			(       self   :							List[str]     , a__   :							str=None		)							->    Optional[int]:





						'''simple docstring'''







						if saveas is None:
										_A       	=      self.saveas
						if saveas.lower().endswith(".jpg"		) or saveas.lower().endswith(".png"		):
										cva.imwrite(
										    a__     , self._get_buffer()[:, :, ::-1]     , )
						else:
										self.fig.savefig(a__		)







		def 			a_			(       self   :							Union[str, Any]     , a__   :							Optional[Any]     , a__   :							List[Any]     , a__   :							Tuple     , a__   :							List[str]		)							->    Optional[int]:





						'''simple docstring'''







						_A       	=      [self.idaobj[i] for i in classes]
						_A       	=      [self.idaattr[i] for i in attr_classes]
						_A       	=      [
						    F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
						    for label, score, attr, attr_score in zip(a__     , a__     , a__     , a__		)
						]
						return labels







		def 			a_			(       self   :							Any     , a__   :							Dict     , a__   :							Tuple		)							->    Dict:





						'''simple docstring'''







						_A       	=      [self.idaobj[i] for i in classes]
						if scores is not None:
										if labels is None:
														_A       	=      ["{:.0f}%".format(s * 1_00		) for s in scores]
										else:
														_A       	=      ["{} {:.0f}%".format(a__     , s * 1_00		) for li, s in zip(a__     , a__		)]
						return labels







		def 			a_			(       self   :							List[Any]     , a__   :							Optional[Any]=2_55		)							->    str:





						'''simple docstring'''







						_A       	=      np.random.randint(0     , len(_COLORS		)		)
						_A       	=      _COLORS[idx] * maximum
						if not self.rgb:
										_A       	=      ret[::-1]
						return ret







		def 			a_			(       self   :							int		)							->    Dict:





						'''simple docstring'''







						if not self.pynb:
										_A	,		_A       	=      self.canvas.print_to_buffer()
										if (width, height) != (self.width, self.height):
														_A       	=      cva.resize(self.img     , (width, height)		)
										else:
														_A       	=      self.img
						else:
										_A       	=      io.BytesIO()  # works for cairo backend
										self.canvas.print_rgba(a__		)
										_A	,		_A       	=      self.width, self.height
										_A       	=      buf.getvalue()
										_A       	=      self.img

						_A       	=      np.frombuffer(a__     , dtype="uint8"		)
						_A       	=      buffer.reshape(a__     , a__     , 4		)
						_A	,		_A       	=      np.split(a__     , [3]     , axis=2		)

						try:
										import numexpr as ne  # fuse them with numexpr

										_A       	=      ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"		)
						except ImportError:
										_A       	=      alpha.astype("float32"		) / 2_5_5.0
										_A       	=      img * (1 - alpha) + rgb * alpha

						return visualized_image.astype("uint8"		)







		def 			a_			(       self   :							Optional[int]     , a__   :							str     , a__   :							Union[str, Any]		)							->    str:





						'''simple docstring'''







						assert brightness_factor >= -1.0 and brightness_factor <= 1.0
						_A       	=      mplc.to_rgb(a__		)
						_A       	=      colorsys.rgb_to_hls(*mplc.to_rgb(a__		)		)
						_A       	=      polygon_color[1] + (brightness_factor * polygon_color[1])
						_A       	=      0.0 if modified_lightness < 0.0 else modified_lightness
						_A       	=      1.0 if modified_lightness > 1.0 else modified_lightness
						_A       	=      colorsys.hls_to_rgb(polygon_color[0]     , a__     , polygon_color[2]		)
						return modified_color





a_											=     (
    np.array(
        [
            0.0_00,
            0.4_47,
            0.7_41,
            0.8_50,
            0.3_25,
            0.0_98,
            0.9_29,
            0.6_94,
            0.1_25,
            0.4_94,
            0.1_84,
            0.5_56,
            0.4_66,
            0.6_74,
            0.1_88,
            0.3_01,
            0.7_45,
            0.9_33,
            0.6_35,
            0.0_78,
            0.1_84,
            0.3_00,
            0.3_00,
            0.3_00,
            0.6_00,
            0.6_00,
            0.6_00,
            1.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.7_49,
            0.7_49,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            0.0_00,
            0.3_33,
            0.6_67,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            0.3_33,
            0.0_00,
            0.6_67,
            0.6_67,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.3_33,
            0.5_00,
            0.0_00,
            0.6_67,
            0.5_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.3_33,
            0.0_00,
            0.5_00,
            0.3_33,
            0.3_33,
            0.5_00,
            0.3_33,
            0.6_67,
            0.5_00,
            0.3_33,
            1.0_00,
            0.5_00,
            0.6_67,
            0.0_00,
            0.5_00,
            0.6_67,
            0.3_33,
            0.5_00,
            0.6_67,
            0.6_67,
            0.5_00,
            0.6_67,
            1.0_00,
            0.5_00,
            1.0_00,
            0.0_00,
            0.5_00,
            1.0_00,
            0.3_33,
            0.5_00,
            1.0_00,
            0.6_67,
            0.5_00,
            1.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            1.0_00,
            0.3_33,
            0.6_67,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.6_67,
            0.3_33,
            1.0_00,
            0.6_67,
            0.6_67,
            1.0_00,
            0.6_67,
            1.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            1.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_43,
            0.1_43,
            0.1_43,
            0.8_57,
            0.8_57,
            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)



"""simple docstring"""


import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


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






def   a__			(					self  ,      __lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase )						->						Tuple:
				_A       	=      image.to(device=__lowercase )
				_A       	=      image  # take image as image_embeddings
				_A       	=      image_embeddings.unsqueeze(1 )

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				_A	,		_A	,		_A       	=      image_embeddings.shape
				_A       	=      image_embeddings.repeat(1  ,      __lowercase  ,      1 )
				_A       	=      image_embeddings.view(bs_embed * num_images_per_prompt  ,      __lowercase  ,      -1 )

				if do_classifier_free_guidance:
								_A       	=      torch.zeros_like(__lowercase )

								# For classifier free guidance, we need to do two forward passes.
								# Here we concatenate the unconditional and text embeddings into a single batch
								# to avoid doing two forward passes
								_A       	=      torch.cat([uncond_embeddings, image_embeddings] )

				return image_embeddings




class snake_case   (	_UpperCamelCase):







		def __init__(       self   :							Dict     , a__   :							PriorTransformer     , a__   :							CLIPTokenizer     , a__   :							CLIPTextModelWithProjection     , a__   :							UnCLIPScheduler     , a__   :							Optional[dict] = None     , )							->    Optional[Any]:





						'''simple docstring'''







						super().__init__()

						_A       	=      {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

						_A       	=      decoder_pipe_kwargs.get("torch_dtype"     , a__		) or prior.dtype

						_A       	=      StableDiffusionImageVariationPipeline.from_pretrained(
						    "lambdalabs/sd-image-variations-diffusers"     , **a__		)

						# replace `_encode_image` method
						_A       	=      types.MethodType(_encode_image     , self.decoder_pipe		)

						self.register_modules(
						    prior=a__     , tokenizer=a__     , text_encoder=a__     , prior_scheduler=a__     , )







		def 			a_			(       self   :							Optional[Any]     , a__   :							str     , a__   :							Optional[Any]     , a__   :							int     , a__   :							Optional[int]     , a__   :							Optional[Union[CLIPTextModelOutput, Tuple]] = None     , a__   :							Optional[torch.Tensor] = None     , )							->    Optional[Any]:





						'''simple docstring'''







						if text_model_output is None:
										_A       	=      len(a__		) if isinstance(a__     , a__		) else 1
										# get prompt text embeddings
										_A       	=      self.tokenizer(
										    a__     , padding="max_length"     , max_length=self.tokenizer.model_max_length     , return_tensors="pt"     , )
										_A       	=      text_inputs.input_ids
										_A       	=      text_inputs.attention_mask.bool().to(a__		)

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
														_A       	=      self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]		)
														logger.warning(
														    "The following part of your input was truncated because CLIP can only handle sequences up to"
														    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""		)
														_A       	=      text_input_ids[:, : self.tokenizer.model_max_length]

										_A       	=      self.text_encoder(text_input_ids.to(a__		)		)

										_A       	=      text_encoder_output.text_embeds
										_A       	=      text_encoder_output.last_hidden_state

						else:
										_A       	=      text_model_output[0].shape[0]
										_A	,		_A       	=      text_model_output[0], text_model_output[1]
										_A       	=      text_attention_mask

						_A       	=      text_embeddings.repeat_interleave(a__     , dim=0		)
						_A       	=      text_encoder_hidden_states.repeat_interleave(a__     , dim=0		)
						_A       	=      text_mask.repeat_interleave(a__     , dim=0		)

						if do_classifier_free_guidance:
										_A       	=      [""] * batch_size

										_A       	=      self.tokenizer(
										    a__     , padding="max_length"     , max_length=self.tokenizer.model_max_length     , truncation=a__     , return_tensors="pt"     , )
										_A       	=      uncond_input.attention_mask.bool().to(a__		)
										_A       	=      self.text_encoder(uncond_input.input_ids.to(a__		)		)

										_A       	=      uncond_embeddings_text_encoder_output.text_embeds
										_A       	=      uncond_embeddings_text_encoder_output.last_hidden_state

										# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

										_A       	=      uncond_embeddings.shape[1]
										_A       	=      uncond_embeddings.repeat(1     , a__		)
										_A       	=      uncond_embeddings.view(batch_size * num_images_per_prompt     , a__		)

										_A       	=      uncond_text_encoder_hidden_states.shape[1]
										_A       	=      uncond_text_encoder_hidden_states.repeat(1     , a__     , 1		)
										_A       	=      uncond_text_encoder_hidden_states.view(
										    batch_size * num_images_per_prompt     , a__     , -1		)
										_A       	=      uncond_text_mask.repeat_interleave(a__     , dim=0		)

										# done duplicates

										# For classifier free guidance, we need to do two forward passes.
										# Here we concatenate the unconditional and text embeddings into a single batch
										# to avoid doing two forward passes
										_A       	=      torch.cat([uncond_embeddings, text_embeddings]		)
										_A       	=      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]		)

										_A       	=      torch.cat([uncond_text_mask, text_mask]		)

						return text_embeddings, text_encoder_hidden_states, text_mask







		@property
		def 			a_			(       self   :							int		)							->    Tuple:





						'''simple docstring'''







						if self.device != torch.device("meta"		) or not hasattr(self.prior     , "_hf_hook"		):
										return self.device
						for module in self.prior.modules():
										if (
										    hasattr(a__     , "_hf_hook"		)
										    and hasattr(module._hf_hook     , "execution_device"		)
										    and module._hf_hook.execution_device is not None
										):
														return torch.device(module._hf_hook.execution_device		)
						return self.device







		def 			a_			(       self   :							str     , a__   :							int     , a__   :							str     , a__   :							List[str]     , a__   :							Dict     , a__   :							Optional[Any]     , a__   :							Dict		)							->    int:





						'''simple docstring'''







						if latents is None:
										_A       	=      randn_tensor(a__     , generator=a__     , device=a__     , dtype=a__		)
						else:
										if latents.shape != shape:
														raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""		)
										_A       	=      latents.to(a__		)

						_A       	=      latents * scheduler.init_noise_sigma
						return latents







		def 			a_			(       self   :							List[str]     , a__   :							Optional[Union[str, torch.device]] = None		)							->    Any:





						'''simple docstring'''







						self.decoder_pipe.to(a__		)
						super().to(a__		)







		@torch.no_grad()
		def __call__(       self   :							List[Any]     , a__   :							Optional[Union[str, List[str]]] = None     , a__   :							Optional[int] = None     , a__   :							Optional[int] = None     , a__   :							int = 1     , a__   :							int = 25     , a__   :							Optional[torch.Generator] = None     , a__   :							Optional[torch.FloatTensor] = None     , a__   :							Optional[Union[CLIPTextModelOutput, Tuple]] = None     , a__   :							Optional[torch.Tensor] = None     , a__   :							float = 4.0     , a__   :							float = 8.0     , a__   :							int = 50     , a__   :							Optional[int] = 1     , a__   :							float = 0.0     , a__   :							Optional[str] = "pil"     , a__   :							bool = True     , )							->    str:





						'''simple docstring'''







						if prompt is not None:
										if isinstance(a__     , a__		):
														_A       	=      1
										elif isinstance(a__     , a__		):
														_A       	=      len(a__		)
										else:
														raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(a__		)}"""		)
						else:
										_A       	=      text_model_output[0].shape[0]

						_A       	=      self._execution_device

						_A       	=      batch_size * num_images_per_prompt

						_A       	=      prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

						_A	,		_A	,		_A       	=      self._encode_prompt(
						    a__     , a__     , a__     , a__     , a__     , a__		)

						# prior

						self.prior_scheduler.set_timesteps(a__     , device=a__		)
						_A       	=      self.prior_scheduler.timesteps

						_A       	=      self.prior.config.embedding_dim

						_A       	=      self.prepare_latents(
						    (batch_size, embedding_dim)     , text_embeddings.dtype     , a__     , a__     , a__     , self.prior_scheduler     , )

						for i, t in enumerate(self.progress_bar(a__		)		):
										# expand the latents if we are doing classifier free guidance
										_A       	=      torch.cat([prior_latents] * 2		) if do_classifier_free_guidance else prior_latents

										_A       	=      self.prior(
										    a__     , timestep=a__     , proj_embedding=a__     , encoder_hidden_states=a__     , attention_mask=a__     , ).predicted_image_embedding

										if do_classifier_free_guidance:
														_A	,		_A       	=      predicted_image_embedding.chunk(2		)
														_A       	=      predicted_image_embedding_uncond + prior_guidance_scale * (
														    predicted_image_embedding_text - predicted_image_embedding_uncond
														)

										if i + 1 == prior_timesteps_tensor.shape[0]:
														_A       	=      None
										else:
														_A       	=      prior_timesteps_tensor[i + 1]

										_A       	=      self.prior_scheduler.step(
										    a__     , timestep=a__     , sample=a__     , generator=a__     , prev_timestep=a__     , ).prev_sample

						_A       	=      self.prior.post_process_latents(a__		)

						_A       	=      prior_latents

						_A       	=      self.decoder_pipe(
						    image=a__     , height=a__     , width=a__     , num_inference_steps=a__     , guidance_scale=a__     , generator=a__     , output_type=a__     , return_dict=a__     , num_images_per_prompt=a__     , eta=a__     , )
						return output