


'''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


__lowercase:		List[str]    						=  1_000







class 			UpperCAmelCase			:

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





    """simple docstring"""



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

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

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

 def 		lowercase_    (			self			:						Optional[int],       a_			:						Union[str, Any],       a_			:						List[str]=None       ):





    """simple docstring"""



    if color is None:
       UpperCamelCase__			    =    self.edgecolor
    ((UpperCamelCase__)    ,		(UpperCamelCase__)    ,		(UpperCamelCase__)    ,		(UpperCamelCase__))			    =    box
    UpperCamelCase__			    =    xa - xa
    UpperCamelCase__			    =    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 		lowercase_    (			self			:						Optional[int],       a_			:						List[Any],       a_			:						Optional[Any]=None,       a_			:						Dict=None,       a_			:						Tuple=None,       a_			:						Dict=None       ):





    """simple docstring"""



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

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

 def 		lowercase_    (			self			:						Dict,       a_			:						Dict,       a_			:						Any,       a_			:						int       ):





    """simple docstring"""



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

    UpperCamelCase__			    =    (ya - ya) / np.sqrt(self.height * self.width       )
    UpperCamelCase__			    =    self._change_color_brightness(a_,       brightness_factor=0.7       )
    UpperCamelCase__			    =    np.clip((height_ratio - 0.02) / 0.08 + 1,       1.2,       2       )
    font_size *= 0.75 * self.font_size

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

 def 		lowercase_    (			self			:						Any,       a_			:						List[Any],       a_			:						str,       a_			:						Dict="g",       a_			:						str="left",       ):





    """simple docstring"""



    UpperCamelCase__			    =    0
    UpperCamelCase__			    =    self.font_size
    UpperCamelCase__			    =    np.maximum(list(mplc.to_rgb(a_       )       ),       0.2       )
    UpperCamelCase__			    =    max(0.8,       np.max(a_       )       )
    UpperCamelCase__			    =    {
        "facecolor": "black",
        "alpha": self.alpha,
        "pad": self.pad,
        "edgecolor": "none",
    }
    UpperCamelCase__    ,		UpperCamelCase__			    =    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 		lowercase_    (			self			:						Dict,       a_			:						List[Any]=None       ):





    """simple docstring"""



    if saveas is None:
       UpperCamelCase__			    =    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 		lowercase_    (			self			:						str,       a_			:						Tuple,       a_			:						List[str],       a_			:						Union[str, Any],       a_			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    [self.idaobj[i] for i in classes]
    UpperCamelCase__			    =    [self.idaattr[i] for i in attr_classes]
    UpperCamelCase__			    =    [
        f'{label} {score:.2f} {attr} {attr_score:.2f}'
        for label, score, attr, attr_score in zip(a_,       a_,       a_,       a_       )
    ]
    return labels

 def 		lowercase_    (			self			:						List[Any],       a_			:						Union[str, Any],       a_			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    [self.idaobj[i] for i in classes]
    if scores is not None:
       if labels is None:
          UpperCamelCase__			    =    ["{:.0f}%".format(s * 100       ) for s in scores]
       else:
          UpperCamelCase__			    =    ["{} {:.0f}%".format(a_,       s * 100       ) for li, s in zip(a_,       a_       )]
    return labels

 def 		lowercase_    (			self			:						Dict,       a_			:						str=255       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



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

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

    try:
       import numexpr as ne  # fuse them with numexpr

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

    return visualized_image.astype("uint8"       )

 def 		lowercase_    (			self			:						Union[str, Any],       a_			:						str,       a_			:						List[Any]       ):





    """simple docstring"""



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







__lowercase:		Optional[Any]    						=  (
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)




# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()



class 		_lowercase		(							UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      StableDiffusionControlNetInpaintPipeline
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _UpperCAmelCase  						=      frozenset({'''control_image'''}						)  # skip `image` and `mask` for now, only test for control_image
   _UpperCAmelCase  						=      TEXT_TO_IMAGE_IMAGE_PARAMS

   def 		A				(   self      :							Dict				)							->					int:


    """simple docstring"""






    torch.manual_seed(0				)
    a							=		UNetaDConditionModel(
        block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=9				, out_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				, cross_attention_dim=32				, )
    torch.manual_seed(0				)
    a							=		ControlNetModel(
        block_out_channels=(32, 64)				, layers_per_block=2				, in_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, cross_attention_dim=32				, conditioning_embedding_out_channels=(16, 32)				, )
    torch.manual_seed(0				)
    a							=		DDIMScheduler(
        beta_start=0.0_0_0_8_5				, beta_end=0.0_1_2				, beta_schedule="scaled_linear"				, clip_sample=__lowerCAmelCase				, set_alpha_to_one=__lowerCAmelCase				, )
    torch.manual_seed(0				)
    a							=		AutoencoderKL(
        block_out_channels=[32, 64]				, in_channels=3				, out_channels=3				, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				, up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				, latent_channels=4				, )
    torch.manual_seed(0				)
    a							=		CLIPTextConfig(
        bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1E-05				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1000				, )
    a							=		CLIPTextModel(__lowerCAmelCase				)
    a							=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

    a							=		{
        "unet": unet,
        "controlnet": controlnet,
        "scheduler": scheduler,
        "vae": vae,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "safety_checker": None,
        "feature_extractor": None,
    }
    return components

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							Tuple=0				)							->					str:


    """simple docstring"""






    if str(__lowerCAmelCase				).startswith("mps"				):
     a							=		torch.manual_seed(__lowerCAmelCase				)
    else:
     a							=		torch.Generator(device=__lowerCAmelCase				).manual_seed(__lowerCAmelCase				)

    a							=		2
    a							=		randn_tensor(
        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=__lowerCAmelCase				, device=torch.device(__lowerCAmelCase				)				, )
    a							=		floats_tensor((1, 3, 32, 32)				, rng=random.Random(__lowerCAmelCase				)				).to(__lowerCAmelCase				)
    a							=		init_image.cpu().permute(0				, 2				, 3				, 1				)[0]

    a							=		Image.fromarray(np.uinta(__lowerCAmelCase				)				).convert("RGB"				).resize((64, 64)				)
    a							=		Image.fromarray(np.uinta(init_image + 4				)				).convert("RGB"				).resize((64, 64)				)

    a							=		{
        "prompt": "A painting of a squirrel eating a burger",
        "generator": generator,
        "num_inference_steps": 2,
        "guidance_scale": 6.0,
        "output_type": "numpy",
        "image": image,
        "mask_image": mask_image,
        "control_image": control_image,
    }

    return inputs

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


    """simple docstring"""






    return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3				)

   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()				, reason="XFormers attention is only available with CUDA and `xformers` installed"				, )
   def 		A				(   self      :							List[Any]				)							->					Tuple:


    """simple docstring"""






    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)




   def 		A				(   self      :							Any				)							->					List[str]:


    """simple docstring"""






    self._test_inference_batch_single_identical(expected_max_diff=2E-3				)



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      StableDiffusionControlNetInpaintPipeline
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _UpperCAmelCase  						=      frozenset([]						)

   def 		A				(   self      :							str				)							->					Optional[int]:


    """simple docstring"""






    torch.manual_seed(0				)
    a							=		UNetaDConditionModel(
        block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=4				, out_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				, cross_attention_dim=32				, )
    torch.manual_seed(0				)
    a							=		ControlNetModel(
        block_out_channels=(32, 64)				, layers_per_block=2				, in_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, cross_attention_dim=32				, conditioning_embedding_out_channels=(16, 32)				, )
    torch.manual_seed(0				)
    a							=		DDIMScheduler(
        beta_start=0.0_0_0_8_5				, beta_end=0.0_1_2				, beta_schedule="scaled_linear"				, clip_sample=__lowerCAmelCase				, set_alpha_to_one=__lowerCAmelCase				, )
    torch.manual_seed(0				)
    a							=		AutoencoderKL(
        block_out_channels=[32, 64]				, in_channels=3				, out_channels=3				, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				, up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				, latent_channels=4				, )
    torch.manual_seed(0				)
    a							=		CLIPTextConfig(
        bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1E-05				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1000				, )
    a							=		CLIPTextModel(__lowerCAmelCase				)
    a							=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

    a							=		{
        "unet": unet,
        "controlnet": controlnet,
        "scheduler": scheduler,
        "vae": vae,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "safety_checker": None,
        "feature_extractor": None,
    }
    return components



class 		_lowercase		(							UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      StableDiffusionControlNetInpaintPipeline
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS

   def 		A				(   self      :							Tuple				)							->					Union[str, Any]:


    """simple docstring"""






    torch.manual_seed(0				)
    a							=		UNetaDConditionModel(
        block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=9				, out_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				, cross_attention_dim=32				, )
    torch.manual_seed(0				)

    def init_weights(__lowerCAmelCase      :							Tuple				):
     if isinstance(__lowerCAmelCase				, torch.nn.Convad				):
      torch.nn.init.normal(m.weight				)
      m.bias.data.fill_(1.0				)

    a							=		ControlNetModel(
        block_out_channels=(32, 64)				, layers_per_block=2				, in_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, cross_attention_dim=32				, conditioning_embedding_out_channels=(16, 32)				, )
    controlneta.controlnet_down_blocks.apply(__lowerCAmelCase				)

    torch.manual_seed(0				)
    a							=		ControlNetModel(
        block_out_channels=(32, 64)				, layers_per_block=2				, in_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, cross_attention_dim=32				, conditioning_embedding_out_channels=(16, 32)				, )
    controlneta.controlnet_down_blocks.apply(__lowerCAmelCase				)

    torch.manual_seed(0				)
    a							=		DDIMScheduler(
        beta_start=0.0_0_0_8_5				, beta_end=0.0_1_2				, beta_schedule="scaled_linear"				, clip_sample=__lowerCAmelCase				, set_alpha_to_one=__lowerCAmelCase				, )
    torch.manual_seed(0				)
    a							=		AutoencoderKL(
        block_out_channels=[32, 64]				, in_channels=3				, out_channels=3				, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				, up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				, latent_channels=4				, )
    torch.manual_seed(0				)
    a							=		CLIPTextConfig(
        bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1E-05				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1000				, )
    a							=		CLIPTextModel(__lowerCAmelCase				)
    a							=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

    a							=		MultiControlNetModel([controlneta, controlneta]				)

    a							=		{
        "unet": unet,
        "controlnet": controlnet,
        "scheduler": scheduler,
        "vae": vae,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "safety_checker": None,
        "feature_extractor": None,
    }
    return components

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							str=0				)							->					str:


    """simple docstring"""






    if str(__lowerCAmelCase				).startswith("mps"				):
     a							=		torch.manual_seed(__lowerCAmelCase				)
    else:
     a							=		torch.Generator(device=__lowerCAmelCase				).manual_seed(__lowerCAmelCase				)

    a							=		2

    a							=		[
        randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=__lowerCAmelCase				, device=torch.device(__lowerCAmelCase				)				, ),
        randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=__lowerCAmelCase				, device=torch.device(__lowerCAmelCase				)				, ),
    ]
    a							=		floats_tensor((1, 3, 32, 32)				, rng=random.Random(__lowerCAmelCase				)				).to(__lowerCAmelCase				)
    a							=		init_image.cpu().permute(0				, 2				, 3				, 1				)[0]

    a							=		Image.fromarray(np.uinta(__lowerCAmelCase				)				).convert("RGB"				).resize((64, 64)				)
    a							=		Image.fromarray(np.uinta(init_image + 4				)				).convert("RGB"				).resize((64, 64)				)

    a							=		{
        "prompt": "A painting of a squirrel eating a burger",
        "generator": generator,
        "num_inference_steps": 2,
        "guidance_scale": 6.0,
        "output_type": "numpy",
        "image": image,
        "mask_image": mask_image,
        "control_image": control_image,
    }

    return inputs

   def 		A				(   self      :							Optional[Any]				)							->					List[str]:


    """simple docstring"""






    a							=		self.get_dummy_components()
    a							=		self.pipeline_class(**__lowerCAmelCase				)
    pipe.to(__lowerCAmelCase				)

    a							=		1_0.0
    a							=		4

    a							=		self.get_dummy_inputs(__lowerCAmelCase				)
    a							=		steps
    a							=		scale
    a							=		pipe(**__lowerCAmelCase				)[0]

    a							=		self.get_dummy_inputs(__lowerCAmelCase				)
    a							=		steps
    a							=		scale
    a							=		pipe(**__lowerCAmelCase				, control_guidance_start=0.1				, control_guidance_end=0.2				)[0]

    a							=		self.get_dummy_inputs(__lowerCAmelCase				)
    a							=		steps
    a							=		scale
    a							=		pipe(**__lowerCAmelCase				, control_guidance_start=[0.1, 0.3]				, control_guidance_end=[0.2, 0.7]				)[0]

    a							=		self.get_dummy_inputs(__lowerCAmelCase				)
    a							=		steps
    a							=		scale
    a							=		pipe(**__lowerCAmelCase				, control_guidance_start=0.4				, control_guidance_end=[0.5, 0.8]				)[0]

    # make sure that all outputs are different
    assert np.sum(np.abs(output_a - output_a				)				) > 1E-3
    assert np.sum(np.abs(output_a - output_a				)				) > 1E-3
    assert np.sum(np.abs(output_a - output_a				)				) > 1E-3

   def 		A				(   self      :							Dict				)							->					List[Any]:


    """simple docstring"""






    return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3				)

   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()				, reason="XFormers attention is only available with CUDA and `xformers` installed"				, )
   def 		A				(   self      :							Optional[int]				)							->					str:


    """simple docstring"""






    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)

   def 		A				(   self      :							Dict				)							->					List[str]:


    """simple docstring"""






    self._test_inference_batch_single_identical(expected_max_diff=2E-3				)




   def 		A				(   self      :							Dict				)							->					List[Any]:


    """simple docstring"""






    a							=		self.get_dummy_components()
    a							=		self.pipeline_class(**__lowerCAmelCase				)
    pipe.to(__lowerCAmelCase				)
    pipe.set_progress_bar_config(disable=__lowerCAmelCase				)
    with tempfile.TemporaryDirectory() as tmpdir:
     try:
      # save_pretrained is not implemented for Multi-ControlNet
      pipe.save_pretrained(__lowerCAmelCase				)
     except NotImplementedError:
      pass



@slow
@require_torch_gpu
class 		_lowercase		(							unittest.TestCase						):

   def 		A				(   self      :							Any				)							->					List[str]:


    """simple docstring"""






    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()

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


    """simple docstring"""






    a							=		ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"				)

    a							=		StableDiffusionControlNetInpaintPipeline.from_pretrained(
        "runwayml/stable-diffusion-inpainting"				, safety_checker=__lowerCAmelCase				, controlnet=__lowerCAmelCase				)
    pipe.enable_model_cpu_offload()
    pipe.set_progress_bar_config(disable=__lowerCAmelCase				)

    a							=		torch.Generator(device="cpu"				).manual_seed(0				)
    a							=		load_image(
        "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"				).resize((512, 512)				)

    a							=		load_image(
        "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
        "/stable_diffusion_inpaint/input_bench_mask.png"				).resize((512, 512)				)

    a							=		"pitch black hole"

    a							=		load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"				).resize((512, 512)				)

    a							=		pipe(
        __lowerCAmelCase				, image=__lowerCAmelCase				, mask_image=__lowerCAmelCase				, control_image=__lowerCAmelCase				, generator=__lowerCAmelCase				, output_type="np"				, num_inference_steps=3				, )

    a							=		output.images[0]

    assert image.shape == (512, 512, 3)

    a							=		load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"				)

    assert np.abs(expected_image - image				).max() < 9E-2




   def 		A				(   self      :							Any				)							->					Dict:


    """simple docstring"""






    a							=		ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"				)

    a							=		StableDiffusionControlNetInpaintPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5"				, safety_checker=__lowerCAmelCase				, controlnet=__lowerCAmelCase				)
    a							=		DDIMScheduler.from_config(pipe.scheduler.config				)
    pipe.enable_model_cpu_offload()
    pipe.set_progress_bar_config(disable=__lowerCAmelCase				)

    a							=		torch.Generator(device="cpu"				).manual_seed(33				)

    a							=		load_image(
        "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"				)
    a							=		init_image.resize((512, 512)				)

    a							=		load_image(
        "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"				)
    a							=		mask_image.resize((512, 512)				)

    a							=		"a handsome man with ray-ban sunglasses"

    def make_inpaint_condition(__lowerCAmelCase      :							Any				, __lowerCAmelCase      :							int				):
     a							=		np.array(image.convert("RGB"				)				).astype(np.floataa				) / 2_5_5.0
     a							=		np.array(image_mask.convert("L"				)				).astype(np.floataa				) / 2_5_5.0

     assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
     a							=		-1.0  # set as masked pixel
     a							=		np.expand_dims(__lowerCAmelCase				, 0				).transpose(0				, 3				, 1				, 2				)
     a							=		torch.from_numpy(__lowerCAmelCase				)
     return image

    a							=		make_inpaint_condition(__lowerCAmelCase				, __lowerCAmelCase				)

    a							=		pipe(
        __lowerCAmelCase				, image=__lowerCAmelCase				, mask_image=__lowerCAmelCase				, control_image=__lowerCAmelCase				, guidance_scale=9.0				, eta=1.0				, generator=__lowerCAmelCase				, num_inference_steps=20				, output_type="np"				, )
    a							=		output.images[0]

    assert image.shape == (512, 512, 3)

    a							=		load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"				)

    assert np.abs(expected_image - image				).max() < 9E-2

