'''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   _lowercase(     _lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,unittest.TestCase				):



   """simple docstring"""





   __lowerCamelCase    =  StableDiffusionControlNetInpaintPipeline
   __lowerCamelCase    =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   __lowerCamelCase    =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   __lowerCamelCase    =  frozenset({'''control_image'''}				)  # skip `image` and `mask` for now, only test for control_image
   __lowerCamelCase    =  TEXT_TO_IMAGE_IMAGE_PARAMS





   def 			snake_case	(							self:    Tuple	):
    torch.manual_seed(0	)
    __UpperCAmelCase         =					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	)
    __UpperCAmelCase         =					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	)
    __UpperCAmelCase         =					DDIMScheduler(
        beta_start=0.00085			,beta_end=0.012			,beta_schedule='scaled_linear'			,clip_sample=a			,set_alpha_to_one=a			,)
    torch.manual_seed(0	)
    __UpperCAmelCase         =					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	)
    __UpperCAmelCase         =					CLIPTextConfig(
        bos_token_id=0			,eos_token_id=2			,hidden_size=32			,intermediate_size=37			,layer_norm_eps=1e-05			,num_attention_heads=4			,num_hidden_layers=5			,pad_token_id=1			,vocab_size=1000			,)
    __UpperCAmelCase         =					CLIPTextModel(a	)
    __UpperCAmelCase         =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

    __UpperCAmelCase         =					{
        '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:    Tuple			,a:    Optional[int]			,a:    List[Any]=0	):
    if str(a	).startswith('mps'	):
     __UpperCAmelCase         =					torch.manual_seed(a	)
    else:
     __UpperCAmelCase         =					torch.Generator(device=a	).manual_seed(a	)

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

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

    __UpperCAmelCase         =					{
        '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:    Tuple	):
    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	):
    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3	)





   def 			snake_case	(							self:    Union[str, Any]	):
    self._test_inference_batch_single_identical(expected_max_diff=2e-3	)




class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  StableDiffusionControlNetInpaintPipeline
   __lowerCamelCase    =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   __lowerCamelCase    =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   __lowerCamelCase    =  frozenset([]				)





   def 			snake_case	(							self:    Dict	):
    torch.manual_seed(0	)
    __UpperCAmelCase         =					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	)
    __UpperCAmelCase         =					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	)
    __UpperCAmelCase         =					DDIMScheduler(
        beta_start=0.00085			,beta_end=0.012			,beta_schedule='scaled_linear'			,clip_sample=a			,set_alpha_to_one=a			,)
    torch.manual_seed(0	)
    __UpperCAmelCase         =					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	)
    __UpperCAmelCase         =					CLIPTextConfig(
        bos_token_id=0			,eos_token_id=2			,hidden_size=32			,intermediate_size=37			,layer_norm_eps=1e-05			,num_attention_heads=4			,num_hidden_layers=5			,pad_token_id=1			,vocab_size=1000			,)
    __UpperCAmelCase         =					CLIPTextModel(a	)
    __UpperCAmelCase         =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

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




class   _lowercase(     _lowerCamelCase   ,_lowerCamelCase   ,unittest.TestCase				):



   """simple docstring"""





   __lowerCamelCase    =  StableDiffusionControlNetInpaintPipeline
   __lowerCamelCase    =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   __lowerCamelCase    =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS





   def 			snake_case	(							self:    str	):
    torch.manual_seed(0	)
    __UpperCAmelCase         =					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:    Any	):
     if isinstance(a			,torch.nn.Convad	):
      torch.nn.init.normal(m.weight	)
      m.bias.data.fill_(1.0	)

    __UpperCAmelCase         =					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	)
    __UpperCAmelCase         =					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	)
    __UpperCAmelCase         =					DDIMScheduler(
        beta_start=0.00085			,beta_end=0.012			,beta_schedule='scaled_linear'			,clip_sample=a			,set_alpha_to_one=a			,)
    torch.manual_seed(0	)
    __UpperCAmelCase         =					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	)
    __UpperCAmelCase         =					CLIPTextConfig(
        bos_token_id=0			,eos_token_id=2			,hidden_size=32			,intermediate_size=37			,layer_norm_eps=1e-05			,num_attention_heads=4			,num_hidden_layers=5			,pad_token_id=1			,vocab_size=1000			,)
    __UpperCAmelCase         =					CLIPTextModel(a	)
    __UpperCAmelCase         =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

    __UpperCAmelCase         =					MultiControlNetModel([controlneta, controlneta]	)

    __UpperCAmelCase         =					{
        '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]			,a:    Dict			,a:    str=0	):
    if str(a	).startswith('mps'	):
     __UpperCAmelCase         =					torch.manual_seed(a	)
    else:
     __UpperCAmelCase         =					torch.Generator(device=a	).manual_seed(a	)

    __UpperCAmelCase         =					2

    __UpperCAmelCase         =					[
        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	)			,),
    ]
    __UpperCAmelCase         =					floats_tensor((1, 3, 32, 32)			,rng=random.Random(a	)	).to(a	)
    __UpperCAmelCase         =					init_image.cpu().permute(0			,2			,3			,1	)[0]

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

    __UpperCAmelCase         =					{
        '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:    Tuple	):
    __UpperCAmelCase         =					self.get_dummy_components()
    __UpperCAmelCase         =					self.pipeline_class(**a	)
    pipe.to(a	)

    __UpperCAmelCase         =					10.0
    __UpperCAmelCase         =					4

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

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

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

    __UpperCAmelCase         =					self.get_dummy_inputs(a	)
    __UpperCAmelCase         =					steps
    __UpperCAmelCase         =					scale
    __UpperCAmelCase         =					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 			snake_case	(							self:    str	):
    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:    List[str]	):
    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3	)





   def 			snake_case	(							self:    Union[str, Any]	):
    self._test_inference_batch_single_identical(expected_max_diff=2e-3	)





   def 			snake_case	(							self:    str	):
    __UpperCAmelCase         =					self.get_dummy_components()
    __UpperCAmelCase         =					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   _lowercase(     unittest.TestCase				):



   """simple docstring"""





   def 			snake_case	(							self:    List[Any]	):
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()





   def 			snake_case	(							self:    Tuple	):
    __UpperCAmelCase         =					ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'	)

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

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

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

    __UpperCAmelCase         =					'pitch black hole'

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

    __UpperCAmelCase         =					pipe(
        a			,image=a			,mask_image=a			,control_image=a			,generator=a			,output_type='np'			,num_inference_steps=3			,)

    __UpperCAmelCase         =					output.images[0]

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

    __UpperCAmelCase         =					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:    str	):
    __UpperCAmelCase         =					ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'	)

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

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

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

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

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

    def make_inpaint_condition(a:    Dict			,a:    List[str]	):
     __UpperCAmelCase         =					np.array(image.convert('RGB'	)	).astype(np.floataa	) / 255.0
     __UpperCAmelCase         =					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         =					-1.0  # set as masked pixel
     __UpperCAmelCase         =					np.expand_dims(a			,0	).transpose(0			,3			,1			,2	)
     __UpperCAmelCase         =					torch.from_numpy(a	)
     return image

    __UpperCAmelCase         =					make_inpaint_condition(a			,a	)

    __UpperCAmelCase         =					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'			,)
    __UpperCAmelCase         =					output.images[0]

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

    __UpperCAmelCase         =					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




'''simple docstring'''




import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
     from .testing_utils import (
         floats_tensor,
         load_hf_numpy,
         load_image,
         load_numpy,
         load_pt,
         nightly,
         parse_flag_from_env,
         print_tensor_test,
         require_torch_a,
         require_torch_gpu,
         skip_mps,
         slow,
         torch_all_close,
         torch_device,
     )
     from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_UpperCamelCase				:		List[Any]       			=		get_logger(__name__)



def 						__snake_case (       lowerCAmelCase       :     List[str]			):
 if version.parse(lowerCAmelCase			) < version.parse(lowerCAmelCase			):
  if "dev" in min_version:
   __UpperCAmelCase         =					(
       'This example requires a source install from HuggingFace diffusers (see '
       '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
   )
  else:
   __UpperCAmelCase         =					F"""This example requires a minimum version of {min_version},"""
  error_message += F""" but the version found is {__version__}.\n"""
  raise ImportError(lowerCAmelCase			)



