






from sklearn.metrics import precision_score

import datasets


__UpperCAmelCase      =				"\nPrecision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation:\nPrecision = TP / (TP + FP)\nwhere TP is the True positives (i.e. the examples correctly labeled as positive) and FP is the False positive examples (i.e. the examples incorrectly labeled as positive).\n"


__UpperCAmelCase      =				"\nArgs:\n    predictions (`list` of `int`): Predicted class labels.\n    references (`list` of `int`): Actual class labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`. If `average` is `None`, it should be the label order. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n    zero_division (`int` or `string`): Sets the value to return when there is a zero division. Defaults to 'warn'.\n\n        - 0: Returns 0 when there is a zero division.\n        - 1: Returns 1 when there is a zero division.\n        - 'warn': Raises warnings and then returns 0 when there is a zero division.\n\nReturns:\n    precision (`float` or `array` of `float`): Precision score or list of precision scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate that fewer negative examples were incorrectly labeled as positive, which means that, generally, higher scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> precision_metric = datasets.load_metric(\"precision\")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {'precision': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> precision_metric = datasets.load_metric(\"precision\")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results['precision'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> precision_metric = datasets.load_metric(\"precision\")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(results)\n        {'precision': 0.23529411764705882}\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average='macro')\n        >>> print(results)\n        {'precision': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average='micro')\n        >>> print(results)\n        {'precision': 0.3333333333333333}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average='weighted')\n        >>> print(results)\n        {'precision': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print([round(res, 2) for res in results['precision']])\n        [0.67, 0.0, 0.0]\n"


__UpperCAmelCase      =				"\n@article{scikit-learn,\n    title={Scikit-learn: Machine Learning in {P}ython},\n    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n    and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n    and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n    Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n    journal={Journal of Machine Learning Research},\n    volume={12},\n    pages={2825--2830},\n    year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,							_KWARGS_DESCRIPTION	)
class 	a_(		datasets.Metric	):



     """simple docstring"""



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

      """simple docstring"""
      return datasets.MetricInfo(
          description=_DESCRIPTION  ,  citation=_CITATION  ,  inputs_description=_KWARGS_DESCRIPTION  ,  features=datasets.Features(
              {
                  'predictions': datasets.Sequence(datasets.Value('int32')),
                  'references': datasets.Sequence(datasets.Value('int32')),
              }
              if self.config_name == 'multilabel'
              else {
                  'predictions': datasets.Value('int32'),
                  'references': datasets.Value('int32'),
              })  ,  reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html']  ,  )





     def     __UpperCamelCase						(		self					:	int  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	Any  ,  lowerCAmelCase__					:	Tuple=None  ,  lowerCAmelCase__					:	List[Any]=1  ,  lowerCAmelCase__					:	Dict="binary"  ,  lowerCAmelCase__					:	int=None  ,  lowerCAmelCase__					:	Tuple="warn"  ,  )							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		precision_score(
          lowerCAmelCase__  ,  lowerCAmelCase__  ,  labels=lowerCAmelCase__  ,  pos_label=lowerCAmelCase__  ,  average=lowerCAmelCase__  ,  sample_weight=lowerCAmelCase__  ,  zero_division=lowerCAmelCase__  ,  )
      return {"precision": float(lowerCAmelCase__) if score.size == 1 else score}









"""simple docstring"""






# 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    __snake_case(					__A			,			__A			,			__A			,			unittest.TestCase       ):
    _A       = StableDiffusionControlNetInpaintPipeline
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    _A       = frozenset({'''control_image'''}       )  # skip `image` and `mask` for now, only test for control_image
    _A       = TEXT_TO_IMAGE_IMAGE_PARAMS




    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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			)
      _SCREAMING_SNAKE_CASE        =			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			)
      _SCREAMING_SNAKE_CASE        =			DDIMScheduler(
          beta_start=0.00_085    ,							beta_end=0.012    ,							beta_schedule='''scaled_linear'''    ,							clip_sample=A_    ,							set_alpha_to_one=A_    ,							)
      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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			)
      _SCREAMING_SNAKE_CASE        =			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=1_000    ,							)
      _SCREAMING_SNAKE_CASE        =			CLIPTextModel(A_			)
      _SCREAMING_SNAKE_CASE        =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

      _SCREAMING_SNAKE_CASE        =			{
          '''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    ,							A_    ,							A_=0			):





      '''simple docstring'''



      if str(A_			).startswith('''mps'''			):
        _SCREAMING_SNAKE_CASE        =			torch.manual_seed(A_			)
      else:
        _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(A_			)

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

      _SCREAMING_SNAKE_CASE        =			Image.fromarray(np.uinta(A_			)			).convert('''RGB'''			).resize((64, 64)			)
      _SCREAMING_SNAKE_CASE        =			Image.fromarray(np.uinta(init_image + 4			)			).convert('''RGB'''			).resize((64, 64)			)

      _SCREAMING_SNAKE_CASE        =			{
          '''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			):





      '''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			):





      '''simple docstring'''



      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3			)




    def   A   (   self			):





      '''simple docstring'''



      self._test_inference_batch_single_identical(expected_max_diff=2e-3			)




class    __snake_case(					__A       ):
    _A       = StableDiffusionControlNetInpaintPipeline
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    _A       = frozenset([]       )




    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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			)
      _SCREAMING_SNAKE_CASE        =			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			)
      _SCREAMING_SNAKE_CASE        =			DDIMScheduler(
          beta_start=0.00_085    ,							beta_end=0.012    ,							beta_schedule='''scaled_linear'''    ,							clip_sample=A_    ,							set_alpha_to_one=A_    ,							)
      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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			)
      _SCREAMING_SNAKE_CASE        =			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=1_000    ,							)
      _SCREAMING_SNAKE_CASE        =			CLIPTextModel(A_			)
      _SCREAMING_SNAKE_CASE        =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

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




class    __snake_case(					__A			,			__A			,			unittest.TestCase       ):
    _A       = StableDiffusionControlNetInpaintPipeline
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS




    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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(A_			):
        if isinstance(A_    ,							torch.nn.Convad			):
          torch.nn.init.normal(m.weight			)
          m.bias.data.fill_(1.0			)

      _SCREAMING_SNAKE_CASE        =			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(A_			)

      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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(A_			)

      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			DDIMScheduler(
          beta_start=0.00_085    ,							beta_end=0.012    ,							beta_schedule='''scaled_linear'''    ,							clip_sample=A_    ,							set_alpha_to_one=A_    ,							)
      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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			)
      _SCREAMING_SNAKE_CASE        =			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=1_000    ,							)
      _SCREAMING_SNAKE_CASE        =			CLIPTextModel(A_			)
      _SCREAMING_SNAKE_CASE        =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

      _SCREAMING_SNAKE_CASE        =			MultiControlNetModel([controlneta, controlneta]			)

      _SCREAMING_SNAKE_CASE        =			{
          '''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    ,							A_    ,							A_=0			):





      '''simple docstring'''



      if str(A_			).startswith('''mps'''			):
        _SCREAMING_SNAKE_CASE        =			torch.manual_seed(A_			)
      else:
        _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(A_			)

      _SCREAMING_SNAKE_CASE        =			2

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

      _SCREAMING_SNAKE_CASE        =			Image.fromarray(np.uinta(A_			)			).convert('''RGB'''			).resize((64, 64)			)
      _SCREAMING_SNAKE_CASE        =			Image.fromarray(np.uinta(init_image + 4			)			).convert('''RGB'''			).resize((64, 64)			)

      _SCREAMING_SNAKE_CASE        =			{
          '''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			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.get_dummy_components()
      _SCREAMING_SNAKE_CASE        =			self.pipeline_class(**A_			)
      pipe.to(A_			)

      _SCREAMING_SNAKE_CASE        =			10.0
      _SCREAMING_SNAKE_CASE        =			4

      _SCREAMING_SNAKE_CASE        =			self.get_dummy_inputs(A_			)
      _SCREAMING_SNAKE_CASE        =			steps
      _SCREAMING_SNAKE_CASE        =			scale
      _SCREAMING_SNAKE_CASE        =			pipe(**A_			)[0]

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

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

      _SCREAMING_SNAKE_CASE        =			self.get_dummy_inputs(A_			)
      _SCREAMING_SNAKE_CASE        =			steps
      _SCREAMING_SNAKE_CASE        =			scale
      _SCREAMING_SNAKE_CASE        =			pipe(**A_    ,							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			):





      '''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			):





      '''simple docstring'''



      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3			)




    def   A   (   self			):





      '''simple docstring'''



      self._test_inference_batch_single_identical(expected_max_diff=2e-3			)




    def   A   (   self			):





      '''simple docstring'''



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




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




    def   A   (   self			):





      '''simple docstring'''



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




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''			)

      _SCREAMING_SNAKE_CASE        =			StableDiffusionControlNetInpaintPipeline.from_pretrained(
          '''runwayml/stable-diffusion-inpainting'''    ,							safety_checker=A_    ,							controlnet=A_			)
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=A_			)

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

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

      _SCREAMING_SNAKE_CASE        =			'''pitch black hole'''

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

      _SCREAMING_SNAKE_CASE        =			pipe(
          A_    ,							image=A_    ,							mask_image=A_    ,							control_image=A_    ,							generator=A_    ,							output_type='''np'''    ,							num_inference_steps=3    ,							)

      _SCREAMING_SNAKE_CASE        =			output.images[0]

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

      _SCREAMING_SNAKE_CASE        =			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			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''			)

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

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device='''cpu'''			).manual_seed(33			)

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

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

      _SCREAMING_SNAKE_CASE        =			'''a handsome man with ray-ban sunglasses'''

      def make_inpaint_condition(A_    ,							A_			):
        _SCREAMING_SNAKE_CASE        =			np.array(image.convert('''RGB'''			)			).astype(np.floataa			) / 255.0
        _SCREAMING_SNAKE_CASE        =			np.array(image_mask.convert('''L'''			)			).astype(np.floataa			) / 255.0

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

      _SCREAMING_SNAKE_CASE        =			make_inpaint_condition(A_    ,							A_			)

      _SCREAMING_SNAKE_CASE        =			pipe(
          A_    ,							image=A_    ,							mask_image=A_    ,							control_image=A_    ,							guidance_scale=9.0    ,							eta=1.0    ,							generator=A_    ,							num_inference_steps=20    ,							output_type='''np'''    ,							)
      _SCREAMING_SNAKE_CASE        =			output.images[0]

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

      _SCREAMING_SNAKE_CASE        =			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
