






"""simple docstring"""



import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets
@contextmanager
def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  def filter_log(__snake_case     :    int       ):
    return False if "This IS expected if you are initializing" in record.msg else True

  lowercase				       =			datasets.utils.logging.get_logger('transformers.modeling_utils'       )
  logger.addFilter(__snake_case       )
  try:
    yield
  finally:
    logger.removeFilter(__snake_case       )


_UpperCamelCase						:    List[str]		=						'\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

_UpperCamelCase						:    Tuple		=						'\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

_UpperCamelCase						:    Dict		=						'\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,							_KWARGS_DESCRIPTION      )
class 			a	(  datasets.Metric      ):







    def      UpperCamelCase_ (     self			):
      return datasets.MetricInfo(
          description=_DESCRIPTION				,						citation=_CITATION				,						homepage='https://github.com/Tiiiger/bert_score'				,						inputs_description=_KWARGS_DESCRIPTION				,						features=datasets.Features(
              {
                  'predictions': datasets.Value('string'				,						id='sequence'			),
                  'references': datasets.Sequence(datasets.Value('string'				,						id='sequence'			)				,						id='references'			),
              }			)				,						codebase_urls=['https://github.com/Tiiiger/bert_score']				,						reference_urls=[
              'https://github.com/Tiiiger/bert_score',
              'https://arxiv.org/abs/1904.09675',
          ]				,						)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=False				,						_lowerCamelCase=False				,						_lowerCamelCase=None				,						_lowerCamelCase=6_4				,						_lowerCamelCase=4				,						_lowerCamelCase=False				,						_lowerCamelCase=False				,						_lowerCamelCase=None				,						_lowerCamelCase=False				,						):
      lowercase				       =			bert_score.utils.get_hash
      lowercase				       =			bert_score.BERTScorer

      if version.parse(bert_score.__version__			) >= version.parse('0.3.10'			):
        lowercase				       =			functools.partial(_lowerCamelCase				,						use_fast_tokenizer=_lowerCamelCase			)
        lowercase				       =			functools.partial(_lowerCamelCase				,						use_fast_tokenizer=_lowerCamelCase			)
      elif use_fast_tokenizer:
        raise ImportWarning(
            'To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'
            'You can install it with `pip install "bert-score>=0.3.10"`.'			)

      if model_type is None:
        assert lang is not None, "either lang or model_type should be specified"
        lowercase				       =			bert_score.utils.langamodel[lang.lower()]

      if num_layers is None:
        lowercase				       =			bert_score.utils.modelalayers[model_type]

      lowercase				       =			get_hash(
          model=_lowerCamelCase				,						num_layers=_lowerCamelCase				,						idf=_lowerCamelCase				,						rescale_with_baseline=_lowerCamelCase				,						use_custom_baseline=baseline_path is not None				,						)

      with filter_logging_context():
        if not hasattr(self				,						'cached_bertscorer'			) or self.cached_bertscorer.hash != hashcode:
          lowercase				       =			scorer(
              model_type=_lowerCamelCase				,						num_layers=_lowerCamelCase				,						batch_size=_lowerCamelCase				,						nthreads=_lowerCamelCase				,						all_layers=_lowerCamelCase				,						idf=_lowerCamelCase				,						device=_lowerCamelCase				,						lang=_lowerCamelCase				,						rescale_with_baseline=_lowerCamelCase				,						baseline_path=_lowerCamelCase				,						)

      ((lowercase)       ,	(lowercase)       ,	(lowercase))				       =			self.cached_bertscorer.score(
          cands=_lowerCamelCase				,						refs=_lowerCamelCase				,						verbose=_lowerCamelCase				,						batch_size=_lowerCamelCase				,						)
      lowercase				       =			{
          'precision': P.tolist(),
          'recall': R.tolist(),
          'f1': F.tolist(),
          'hashcode': hashcode,
      }
      return output_dict







    def      UpperCamelCase_ (     self				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						**_lowerCamelCase			):
      # References can be strings or lists of strings
      # Let's change strings to lists of strings with one element
      if references is not None:
        lowercase				       =			[[ref] if isinstance(_lowerCamelCase				,						_lowerCamelCase			) else ref for ref in references]
      super().add_batch(predictions=_lowerCamelCase				,						references=_lowerCamelCase				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						**_lowerCamelCase			):
      # References can be strings or lists of strings
      # Let's change strings to lists of strings with one element
      if isinstance(_lowerCamelCase				,						_lowerCamelCase			):
        lowercase				       =			[reference]
      super().add(prediction=_lowerCamelCase				,						reference=_lowerCamelCase				,						**_lowerCamelCase			)







# 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

