





"""simple docstring"""




from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


UpperCamelCase_	 =logging.get_logger(__name__)

UpperCamelCase_	 ={"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase_	 ={
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCamelCase_	 ={
    """openai/clip-vit-base-patch32""": 77,
}




class 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       VOCAB_FILES_NAMES
				UpperCamelCase								=       PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase								=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase								=       ['''input_ids''', '''attention_mask''']
				UpperCamelCase								=       CLIPTokenizer






				def __init__(					self						:       str,					lowerCAmelCase__						:       List[str]=None,					lowerCAmelCase__						:       Any=None,					lowerCAmelCase__						:       Tuple=None,					lowerCAmelCase__						:       List[str]="<|endoftext|>",					lowerCAmelCase__						:       int="<|startoftext|>",					lowerCAmelCase__						:       Union[str, Any]="<|endoftext|>",					lowerCAmelCase__						:       Optional[int]="<|endoftext|>",					**lowerCAmelCase__						:       str,					)      -> str:




										'''simple docstring'''



										super().__init__(
										    lowerCAmelCase__,					lowerCAmelCase__,					tokenizer_file=lowerCAmelCase__,					unk_token=lowerCAmelCase__,					bos_token=lowerCAmelCase__,					eos_token=lowerCAmelCase__,					pad_token=lowerCAmelCase__,					**lowerCAmelCase__,					)

										if not isinstance(self.backend_tokenizer.pre_tokenizer,					pre_tokenizers.Sequence				):
																raise ValueError(
																    '''The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'''
																    ''' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'''
																    ''' to be compatible with this version.The easiest way to do so is'''
																    ''' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'''
																    ''' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'''
																    ''' transformers.'''				)

										self._wrap_decode_method_backend_tokenizer()






				def 							snake_case      (					self						:       Dict				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.backend_tokenizer.decode

										def new_decode_method(*lowerCAmelCase__						:       List[str],					**lowerCAmelCase__						:       int				):
																_UpperCamelCase     :   str      =       orig_decode_method(*lowerCAmelCase__,					**lowerCAmelCase__				)
																_UpperCamelCase     :   List[str]      =       text.replace(self.backend_tokenizer.model.end_of_word_suffix,					''' '''				).strip()
																return text

										_UpperCamelCase     :   Optional[Any]      =       new_decode_method






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       List[int],					lowerCAmelCase__						:       Optional[List[int]] = None				)      -> List[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       [self.bos_token_id]
										_UpperCamelCase     :   Any      =       [self.eos_token_id]

										if token_ids_a is None:
																return bos_token + token_ids_a + eos_token
										return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       List[int],					lowerCAmelCase__						:       Optional[List[int]] = None				)      -> List[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       [self.bos_token_id]
										_UpperCamelCase     :   Optional[int]      =       [self.eos_token_id]

										if token_ids_a is None:
																return len(bos_token + token_ids_a + eos_token				) * [0]
										return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token				) * [0]






				def 							snake_case      (					self						:       List[str],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Optional[str] = None				)      -> Tuple[str]:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       self._tokenizer.model.save(lowerCAmelCase__,					name=lowerCAmelCase__				)
										return tuple(lowerCAmelCase__				)










"""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 					_a		( _lowerCAmelCase      ,							_lowerCAmelCase      ,							_lowerCAmelCase      ,							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 							snake_case      (					self						:       Tuple				)      -> Any:




										'''simple docstring'''



										torch.manual_seed(0				)
										_UpperCamelCase     :   Tuple      =       UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4),					layers_per_block=2,					sample_size=3_2,					in_channels=9,					out_channels=4,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),					cross_attention_dim=3_2,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   List[str]      =       ControlNetModel(
										    block_out_channels=(3_2, 6_4),					layers_per_block=2,					in_channels=4,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),					cross_attention_dim=3_2,					conditioning_embedding_out_channels=(1_6, 3_2),					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   Optional[Any]      =       DDIMScheduler(
										    beta_start=0.00_085,					beta_end=0.012,					beta_schedule='''scaled_linear''',					clip_sample=lowerCAmelCase__,					set_alpha_to_one=lowerCAmelCase__,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   Any      =       AutoencoderKL(
										    block_out_channels=[3_2, 6_4],					in_channels=3,					out_channels=3,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],					latent_channels=4,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   List[str]      =       CLIPTextConfig(
										    bos_token_id=0,					eos_token_id=2,					hidden_size=3_2,					intermediate_size=3_7,					layer_norm_eps=1e-0_5,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1_0_0_0,					)
										_UpperCamelCase     :   List[str]      =       CLIPTextModel(lowerCAmelCase__				)
										_UpperCamelCase     :   Dict      =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Optional[int]=0				)      -> Optional[int]:




										'''simple docstring'''



										if str(lowerCAmelCase__				).startswith('''mps'''				):
																_UpperCamelCase     :   str      =       torch.manual_seed(lowerCAmelCase__				)
										else:
																_UpperCamelCase     :   Any      =       torch.Generator(device=lowerCAmelCase__				).manual_seed(lowerCAmelCase__				)

										_UpperCamelCase     :   Optional[Any]      =       2
										_UpperCamelCase     :   int      =       randn_tensor(
										    (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor),					generator=lowerCAmelCase__,					device=torch.device(lowerCAmelCase__				),					)
										_UpperCamelCase     :   int      =       floats_tensor((1, 3, 3_2, 3_2),					rng=random.Random(lowerCAmelCase__				)				).to(lowerCAmelCase__				)
										_UpperCamelCase     :   List[str]      =       init_image.cpu().permute(0,					2,					3,					1				)[0]

										_UpperCamelCase     :   Optional[int]      =       Image.fromarray(np.uinta(lowerCAmelCase__				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
										_UpperCamelCase     :   Any      =       Image.fromarray(np.uinta(init_image + 4				)				).convert('''RGB'''				).resize((6_4, 6_4)				)

										_UpperCamelCase     :   Tuple      =       {
										    '''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 							snake_case      (					self						:       int				)      -> Union[str, 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 							snake_case      (					self						:       Dict				)      -> int:




										'''simple docstring'''



										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3				)






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




										'''simple docstring'''



										self._test_inference_batch_single_identical(expected_max_diff=2e-3				)




class 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       StableDiffusionControlNetInpaintPipeline
				UpperCamelCase								=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				UpperCamelCase								=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				UpperCamelCase								=       frozenset([]							)






				def 							snake_case      (					self						:       List[Any]				)      -> List[Any]:




										'''simple docstring'''



										torch.manual_seed(0				)
										_UpperCamelCase     :   List[Any]      =       UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4),					layers_per_block=2,					sample_size=3_2,					in_channels=4,					out_channels=4,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),					cross_attention_dim=3_2,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   Optional[int]      =       ControlNetModel(
										    block_out_channels=(3_2, 6_4),					layers_per_block=2,					in_channels=4,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),					cross_attention_dim=3_2,					conditioning_embedding_out_channels=(1_6, 3_2),					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   List[str]      =       DDIMScheduler(
										    beta_start=0.00_085,					beta_end=0.012,					beta_schedule='''scaled_linear''',					clip_sample=lowerCAmelCase__,					set_alpha_to_one=lowerCAmelCase__,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   Optional[int]      =       AutoencoderKL(
										    block_out_channels=[3_2, 6_4],					in_channels=3,					out_channels=3,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],					latent_channels=4,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   List[str]      =       CLIPTextConfig(
										    bos_token_id=0,					eos_token_id=2,					hidden_size=3_2,					intermediate_size=3_7,					layer_norm_eps=1e-0_5,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1_0_0_0,					)
										_UpperCamelCase     :   int      =       CLIPTextModel(lowerCAmelCase__				)
										_UpperCamelCase     :   int      =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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




class 					_a		( _lowerCAmelCase      ,							_lowerCAmelCase      ,							unittest.TestCase							):
				UpperCamelCase								=       StableDiffusionControlNetInpaintPipeline
				UpperCamelCase								=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				UpperCamelCase								=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






				def 							snake_case      (					self						:       Any				)      -> Dict:




										'''simple docstring'''



										torch.manual_seed(0				)
										_UpperCamelCase     :   int      =       UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4),					layers_per_block=2,					sample_size=3_2,					in_channels=9,					out_channels=4,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),					cross_attention_dim=3_2,					)
										torch.manual_seed(0				)

										def init_weights(lowerCAmelCase__						:       Optional[Any]				):
																if isinstance(lowerCAmelCase__,					torch.nn.Convad				):
																						torch.nn.init.normal(m.weight				)
																						m.bias.data.fill_(1.0				)

										_UpperCamelCase     :   List[Any]      =       ControlNetModel(
										    block_out_channels=(3_2, 6_4),					layers_per_block=2,					in_channels=4,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),					cross_attention_dim=3_2,					conditioning_embedding_out_channels=(1_6, 3_2),					)
										controlneta.controlnet_down_blocks.apply(lowerCAmelCase__				)

										torch.manual_seed(0				)
										_UpperCamelCase     :   int      =       ControlNetModel(
										    block_out_channels=(3_2, 6_4),					layers_per_block=2,					in_channels=4,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),					cross_attention_dim=3_2,					conditioning_embedding_out_channels=(1_6, 3_2),					)
										controlneta.controlnet_down_blocks.apply(lowerCAmelCase__				)

										torch.manual_seed(0				)
										_UpperCamelCase     :   Tuple      =       DDIMScheduler(
										    beta_start=0.00_085,					beta_end=0.012,					beta_schedule='''scaled_linear''',					clip_sample=lowerCAmelCase__,					set_alpha_to_one=lowerCAmelCase__,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   str      =       AutoencoderKL(
										    block_out_channels=[3_2, 6_4],					in_channels=3,					out_channels=3,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],					latent_channels=4,					)
										torch.manual_seed(0				)
										_UpperCamelCase     :   List[Any]      =       CLIPTextConfig(
										    bos_token_id=0,					eos_token_id=2,					hidden_size=3_2,					intermediate_size=3_7,					layer_norm_eps=1e-0_5,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1_0_0_0,					)
										_UpperCamelCase     :   Any      =       CLIPTextModel(lowerCAmelCase__				)
										_UpperCamelCase     :   Union[str, Any]      =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

										_UpperCamelCase     :   str      =       MultiControlNetModel([controlneta, controlneta]				)

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






				def 							snake_case      (					self						:       Union[str, Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Tuple=0				)      -> str:




										'''simple docstring'''



										if str(lowerCAmelCase__				).startswith('''mps'''				):
																_UpperCamelCase     :   List[Any]      =       torch.manual_seed(lowerCAmelCase__				)
										else:
																_UpperCamelCase     :   List[str]      =       torch.Generator(device=lowerCAmelCase__				).manual_seed(lowerCAmelCase__				)

										_UpperCamelCase     :   Any      =       2

										_UpperCamelCase     :   Union[str, Any]      =       [
										    randn_tensor(
										        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor),					generator=lowerCAmelCase__,					device=torch.device(lowerCAmelCase__				),					),
										    randn_tensor(
										        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor),					generator=lowerCAmelCase__,					device=torch.device(lowerCAmelCase__				),					),
										]
										_UpperCamelCase     :   int      =       floats_tensor((1, 3, 3_2, 3_2),					rng=random.Random(lowerCAmelCase__				)				).to(lowerCAmelCase__				)
										_UpperCamelCase     :   int      =       init_image.cpu().permute(0,					2,					3,					1				)[0]

										_UpperCamelCase     :   List[str]      =       Image.fromarray(np.uinta(lowerCAmelCase__				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
										_UpperCamelCase     :   int      =       Image.fromarray(np.uinta(init_image + 4				)				).convert('''RGB'''				).resize((6_4, 6_4)				)

										_UpperCamelCase     :   Dict      =       {
										    '''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 							snake_case      (					self						:       Optional[int]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       self.get_dummy_components()
										_UpperCamelCase     :   Optional[int]      =       self.pipeline_class(**lowerCAmelCase__				)
										pipe.to(lowerCAmelCase__				)

										_UpperCamelCase     :   int      =       10.0
										_UpperCamelCase     :   List[Any]      =       4

										_UpperCamelCase     :   List[str]      =       self.get_dummy_inputs(lowerCAmelCase__				)
										_UpperCamelCase     :   Union[str, Any]      =       steps
										_UpperCamelCase     :   Union[str, Any]      =       scale
										_UpperCamelCase     :   List[Any]      =       pipe(**lowerCAmelCase__				)[0]

										_UpperCamelCase     :   Dict      =       self.get_dummy_inputs(lowerCAmelCase__				)
										_UpperCamelCase     :   List[str]      =       steps
										_UpperCamelCase     :   Union[str, Any]      =       scale
										_UpperCamelCase     :   Union[str, Any]      =       pipe(**lowerCAmelCase__,					control_guidance_start=0.1,					control_guidance_end=0.2				)[0]

										_UpperCamelCase     :   int      =       self.get_dummy_inputs(lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       steps
										_UpperCamelCase     :   Optional[Any]      =       scale
										_UpperCamelCase     :   Optional[int]      =       pipe(**lowerCAmelCase__,					control_guidance_start=[0.1, 0.3],					control_guidance_end=[0.2, 0.7]				)[0]

										_UpperCamelCase     :   Tuple      =       self.get_dummy_inputs(lowerCAmelCase__				)
										_UpperCamelCase     :   Tuple      =       steps
										_UpperCamelCase     :   int      =       scale
										_UpperCamelCase     :   Optional[Any]      =       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 							snake_case      (					self						:       Union[str, Any]				)      -> Tuple:




										'''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 							snake_case      (					self						:       str				)      -> Union[str, Any]:




										'''simple docstring'''



										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3				)






				def 							snake_case      (					self						:       List[str]				)      -> Tuple:




										'''simple docstring'''



										self._test_inference_batch_single_identical(expected_max_diff=2e-3				)






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




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.get_dummy_components()
										_UpperCamelCase     :   Union[str, Any]      =       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 					_a		( unittest.TestCase							):






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




										'''simple docstring'''



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






				def 							snake_case      (					self						:       Any				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''				)

										_UpperCamelCase     :   int      =       StableDiffusionControlNetInpaintPipeline.from_pretrained(
										    '''runwayml/stable-diffusion-inpainting''',					safety_checker=lowerCAmelCase__,					controlnet=lowerCAmelCase__				)
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

										_UpperCamelCase     :   str      =       torch.Generator(device='''cpu'''				).manual_seed(0				)
										_UpperCamelCase     :   Optional[int]      =       load_image(
										    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''				).resize((5_1_2, 5_1_2)				)

										_UpperCamelCase     :   List[str]      =       load_image(
										    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
										    '''/stable_diffusion_inpaint/input_bench_mask.png'''				).resize((5_1_2, 5_1_2)				)

										_UpperCamelCase     :   int      =       '''pitch black hole'''

										_UpperCamelCase     :   List[Any]      =       load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''				).resize((5_1_2, 5_1_2)				)

										_UpperCamelCase     :   List[Any]      =       pipe(
										    lowerCAmelCase__,					image=lowerCAmelCase__,					mask_image=lowerCAmelCase__,					control_image=lowerCAmelCase__,					generator=lowerCAmelCase__,					output_type='''np''',					num_inference_steps=3,					)

										_UpperCamelCase     :   Union[str, Any]      =       output.images[0]

										assert image.shape == (5_1_2, 5_1_2, 3)

										_UpperCamelCase     :   Union[str, Any]      =       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 							snake_case      (					self						:       List[str]				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''				)

										_UpperCamelCase     :   Tuple      =       StableDiffusionControlNetInpaintPipeline.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5''',					safety_checker=lowerCAmelCase__,					controlnet=lowerCAmelCase__				)
										_UpperCamelCase     :   int      =       DDIMScheduler.from_config(pipe.scheduler.config				)
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

										_UpperCamelCase     :   Any      =       torch.Generator(device='''cpu'''				).manual_seed(3_3				)

										_UpperCamelCase     :   Union[str, Any]      =       load_image(
										    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'''				)
										_UpperCamelCase     :   Optional[int]      =       init_image.resize((5_1_2, 5_1_2)				)

										_UpperCamelCase     :   List[Any]      =       load_image(
										    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'''				)
										_UpperCamelCase     :   Dict      =       mask_image.resize((5_1_2, 5_1_2)				)

										_UpperCamelCase     :   Tuple      =       '''a handsome man with ray-ban sunglasses'''

										def make_inpaint_condition(lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Optional[Any]				):
																_UpperCamelCase     :   Union[str, Any]      =       np.array(image.convert('''RGB'''				)				).astype(np.floataa				) / 255.0
																_UpperCamelCase     :   Optional[int]      =       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"
																_UpperCamelCase     :   Union[str, Any]      =       -1.0  # set as masked pixel
																_UpperCamelCase     :   Union[str, Any]      =       np.expand_dims(lowerCAmelCase__,					0				).transpose(0,					3,					1,					2				)
																_UpperCamelCase     :   List[Any]      =       torch.from_numpy(lowerCAmelCase__				)
																return image

										_UpperCamelCase     :   Any      =       make_inpaint_condition(lowerCAmelCase__,					lowerCAmelCase__				)

										_UpperCamelCase     :   Optional[int]      =       pipe(
										    lowerCAmelCase__,					image=lowerCAmelCase__,					mask_image=lowerCAmelCase__,					control_image=lowerCAmelCase__,					guidance_scale=9.0,					eta=1.0,					generator=lowerCAmelCase__,					num_inference_steps=2_0,					output_type='''np''',					)
										_UpperCamelCase     :   Union[str, Any]      =       output.images[0]

										assert image.shape == (5_1_2, 5_1_2, 3)

										_UpperCamelCase     :   List[str]      =       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



