



'''simple docstring'''







import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      import PIL

      from transformers import FlavaImageProcessor
      from transformers.image_utils import PILImageResampling
      from transformers.models.flava.image_processing_flava import (
          FLAVA_CODEBOOK_MEAN,
          FLAVA_CODEBOOK_STD,
          FLAVA_IMAGE_MEAN,
          FLAVA_IMAGE_STD,
      )
else:
      lowercase__  :	List[str]            =      None

class    lowerCamelCase_				(						unittest.TestCase	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=7  ,    __UpperCamelCase=3  ,    __UpperCamelCase=30  ,    __UpperCamelCase=4_00  ,    __UpperCamelCase=True  ,    __UpperCamelCase=None  ,    __UpperCamelCase=True  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    __UpperCamelCase=True  ,    __UpperCamelCase=1 / 2_55  ,    __UpperCamelCase=True  ,    __UpperCamelCase=FLAVA_IMAGE_MEAN  ,    __UpperCamelCase=FLAVA_IMAGE_STD  ,    __UpperCamelCase=14  ,    __UpperCamelCase=75  ,    __UpperCamelCase=None  ,    __UpperCamelCase=16  ,    __UpperCamelCase=0.3  ,    __UpperCamelCase=None  ,    __UpperCamelCase=True  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    __UpperCamelCase=True  ,    __UpperCamelCase=None  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase=FLAVA_CODEBOOK_MEAN  ,    __UpperCamelCase=FLAVA_CODEBOOK_STD  ,    ):
       UpperCamelCase__	     =      size if size is not None else {"height": 2_24, "width": 2_24}
       UpperCamelCase__	     =      crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
       UpperCamelCase__	     =      codebook_size if codebook_size is not None else {"height": 1_12, "width": 1_12}
       UpperCamelCase__	     =      codebook_crop_size if codebook_crop_size is not None else {"height": 1_12, "width": 1_12}

       UpperCamelCase__	     =      parent
       UpperCamelCase__	     =      batch_size
       UpperCamelCase__	     =      num_channels
       UpperCamelCase__	     =      do_resize
       UpperCamelCase__	     =      do_rescale
       UpperCamelCase__	     =      rescale_factor
       UpperCamelCase__	     =      min_resolution
       UpperCamelCase__	     =      max_resolution
       UpperCamelCase__	     =      size
       UpperCamelCase__	     =      resample if resample is not None else PILImageResampling.BICUBIC
       UpperCamelCase__	     =      do_normalize
       UpperCamelCase__	     =      image_mean
       UpperCamelCase__	     =      image_std
       UpperCamelCase__	     =      do_center_crop
       UpperCamelCase__	     =      crop_size

       UpperCamelCase__	     =      input_size_patches
       UpperCamelCase__	     =      total_mask_patches
       UpperCamelCase__	     =      mask_group_max_patches
       UpperCamelCase__	     =      mask_group_min_patches
       UpperCamelCase__	     =      mask_group_min_aspect_ratio
       UpperCamelCase__	     =      mask_group_max_aspect_ratio

       UpperCamelCase__	     =      codebook_do_resize
       UpperCamelCase__	     =      codebook_size
       UpperCamelCase__	     =      codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
       UpperCamelCase__	     =      codebook_do_center_crop
       UpperCamelCase__	     =      codebook_crop_size
       UpperCamelCase__	     =      codebook_do_map_pixels
       UpperCamelCase__	     =      codebook_do_normalize
       UpperCamelCase__	     =      codebook_image_mean
       UpperCamelCase__	     =      codebook_image_std


   def 						_UpperCamelCase							(	self			):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "resample": self.resample,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "input_size_patches": self.input_size_patches,
           "total_mask_patches": self.total_mask_patches,
           "mask_group_max_patches": self.mask_group_max_patches,
           "mask_group_min_patches": self.mask_group_min_patches,
           "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
           "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
           "codebook_do_resize": self.codebook_do_resize,
           "codebook_size": self.codebook_size,
           "codebook_resample": self.codebook_resample,
           "codebook_do_center_crop": self.codebook_do_center_crop,
           "codebook_crop_size": self.codebook_crop_size,
           "codebook_do_map_pixels": self.codebook_do_map_pixels,
           "codebook_do_normalize": self.codebook_do_normalize,
           "codebook_image_mean": self.codebook_image_mean,
           "codebook_image_std": self.codebook_image_std,
       }


   def 						_UpperCamelCase							(	self			):
       return (self.size["height"], self.size["width"])


   def 						_UpperCamelCase							(	self			):
       return (
           (self.input_size_patches, self.input_size_patches)
           if not isinstance(self.input_size_patches  ,    A_			)
           else self.input_size_patches
       )







   def 						_UpperCamelCase							(	self			):
       return (self.codebook_size["height"], self.codebook_size["width"])

@require_torch
@require_vision
class    lowerCamelCase_				(						__A       ,     unittest.TestCase	):






   '''simple docstring'''

   A			:  Optional[Any]					=						FlavaImageProcessor if is_vision_available() else None
   A			:  Optional[int]					=						None


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      FlavaImageProcessingTester(self			)


   @property
   def 						_UpperCamelCase							(	self			):
       return self.image_processor_tester.prepare_image_processor_dict()


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(A_  ,    "image_mean"			)			)
       self.assertTrue(hasattr(A_  ,    "image_std"			)			)
       self.assertTrue(hasattr(A_  ,    "do_normalize"			)			)
       self.assertTrue(hasattr(A_  ,    "do_resize"			)			)
       self.assertTrue(hasattr(A_  ,    "resample"			)			)
       self.assertTrue(hasattr(A_  ,    "crop_size"			)			)
       self.assertTrue(hasattr(A_  ,    "do_center_crop"			)			)
       self.assertTrue(hasattr(A_  ,    "do_rescale"			)			)
       self.assertTrue(hasattr(A_  ,    "rescale_factor"			)			)
       self.assertTrue(hasattr(A_  ,    "masking_generator"			)			)
       self.assertTrue(hasattr(A_  ,    "codebook_do_resize"			)			)
       self.assertTrue(hasattr(A_  ,    "codebook_size"			)			)
       self.assertTrue(hasattr(A_  ,    "codebook_resample"			)			)
       self.assertTrue(hasattr(A_  ,    "codebook_do_center_crop"			)			)
       self.assertTrue(hasattr(A_  ,    "codebook_crop_size"			)			)
       self.assertTrue(hasattr(A_  ,    "codebook_do_map_pixels"			)			)
       self.assertTrue(hasattr(A_  ,    "codebook_do_normalize"			)			)
       self.assertTrue(hasattr(A_  ,    "codebook_image_mean"			)			)
       self.assertTrue(hasattr(A_  ,    "codebook_image_std"			)			)


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.image_processing_class.from_dict(self.image_processor_dict			)
       self.assertEqual(image_processor.size  ,    {"height": 2_24, "width": 2_24}			)
       self.assertEqual(image_processor.crop_size  ,    {"height": 2_24, "width": 2_24}			)
       self.assertEqual(image_processor.codebook_size  ,    {"height": 1_12, "width": 1_12}			)
       self.assertEqual(image_processor.codebook_crop_size  ,    {"height": 1_12, "width": 1_12}			)

       UpperCamelCase__	     =      self.image_processing_class.from_dict(
           self.image_processor_dict  ,    size=42  ,    crop_size=84  ,    codebook_size=33  ,    codebook_crop_size=66			)
       self.assertEqual(image_processor.size  ,    {"height": 42, "width": 42}			)
       self.assertEqual(image_processor.crop_size  ,    {"height": 84, "width": 84}			)
       self.assertEqual(image_processor.codebook_size  ,    {"height": 33, "width": 33}			)
       self.assertEqual(image_processor.codebook_crop_size  ,    {"height": 66, "width": 66}			)


   def 						_UpperCamelCase							(	self			):
       pass


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       UpperCamelCase__	     =      prepare_image_inputs(self.image_processor_tester  ,    equal_resolution=A_			)
       for image in image_inputs:
           self.assertIsInstance(A_  ,    PIL.Image.Image			)

       # Test not batched input
       UpperCamelCase__	     =      image_processing(image_inputs[0]  ,    return_tensors="pt"			)

       # Test no bool masked pos
       self.assertFalse("bool_masked_pos" in encoded_images			)

       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_image_size()

       self.assertEqual(
           encoded_images.pixel_values.shape  ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,    )

       # Test batched
       UpperCamelCase__	     =      image_processing(A_  ,    return_tensors="pt"			)
       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_image_size()

       # Test no bool masked pos
       self.assertFalse("bool_masked_pos" in encoded_images			)

       self.assertEqual(
           encoded_images.pixel_values.shape  ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )  ,    )


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      self.image_processing_class(**self.image_processor_dict			)
       # create random tensors
       UpperCamelCase__	     =      prepare_image_inputs(self.image_processor_tester  ,    equal_resolution=A_  ,    **A_			)
       for image in image_inputs:
           self.assertIsInstance(A_  ,    A_			)

       # Test not batched input
       UpperCamelCase__	     =      image_processing(image_inputs[0]  ,    return_tensors="pt"			)

       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.pixel_values.shape  ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,    )

       UpperCamelCase__	     =      image_processing(A_  ,    return_image_mask=A_  ,    return_tensors="pt"			)

       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.pixel_values.shape  ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )  ,    )

       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_mask_size()
       self.assertEqual(
           encoded_images.bool_masked_pos.shape  ,    (
               self.image_processor_tester.batch_size,
               expected_height,
               expected_width,
           )  ,    )

       # Test batched
       UpperCamelCase__	     =      image_processing(A_  ,    return_tensors="pt"			).pixel_values

       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.shape  ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )  ,    )

       # Test masking
       UpperCamelCase__	     =      image_processing(A_  ,    return_image_mask=A_  ,    return_tensors="pt"			)

       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.pixel_values.shape  ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )  ,    )

       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_mask_size()
       self.assertEqual(
           encoded_images.bool_masked_pos.shape  ,    (
               self.image_processor_tester.batch_size,
               expected_height,
               expected_width,
           )  ,    )


   def 						_UpperCamelCase							(	self			):
       self._test_call_framework(np.ndarray  ,    prepare_kwargs={"numpify": True}			)


   def 						_UpperCamelCase							(	self			):
       self._test_call_framework(torch.Tensor  ,    prepare_kwargs={"torchify": True}			)


   def 						_UpperCamelCase							(	self			):
       random.seed(12_34			)
       UpperCamelCase__	     =      self.image_processing_class(**self.image_processor_dict			)
       UpperCamelCase__	     =      prepare_image_inputs(self.image_processor_tester  ,    equal_resolution=A_  ,    torchify=A_			)

       # Test not batched input
       UpperCamelCase__	     =      image_processing(image_inputs[0]  ,    return_image_mask=A_  ,    return_tensors="pt"			)
       self.assertEqual(encoded_images.bool_masked_pos.sum().item()  ,    75			)







   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       UpperCamelCase__	     =      prepare_image_inputs(self.image_processor_tester  ,    equal_resolution=A_			)
       for image in image_inputs:
           self.assertIsInstance(A_  ,    PIL.Image.Image			)

       # Test not batched input
       UpperCamelCase__	     =      image_processing(image_inputs[0]  ,    return_codebook_pixels=A_  ,    return_tensors="pt"			)
       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_codebook_image_size()
       self.assertEqual(
           encoded_images.codebook_pixel_values.shape  ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,    )

       # Test batched
       UpperCamelCase__	     =      image_processing(A_  ,    return_codebook_pixels=A_  ,    return_tensors="pt"			)
       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_codebook_image_size()
       self.assertEqual(
           encoded_images.codebook_pixel_values.shape  ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )  ,    )









"""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
