"""simple docstring"""



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 SCREAMING_SNAKE_CASE     (    a   ,      a   ,      a   ,      unittest.TestCase	):







 """simple docstring"""


 a_       :	List[str]  			=StableDiffusionControlNetInpaintPipeline
 a_       :	Tuple  			=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 a_       :	Optional[int]  			=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 a_       :	Any  			=frozenset({"control_image"}	)  # skip `image` and `mask` for now, only test for control_image
 a_       :	Any  			=TEXT_TO_IMAGE_IMAGE_PARAMS


 def        _lowerCAmelCase				(   self		:      Optional[Any]				)				->			Tuple:




    '''simple docstring'''






    torch.manual_seed(0				)
    a__     				=		UNetaDConditionModel(
        block_out_channels=(32, 64)					,   layers_per_block=2					,   sample_size=32					,   in_channels=9					,   out_channels=4					,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,   cross_attention_dim=32					,   )
    torch.manual_seed(0				)
    a__     				=		ControlNetModel(
        block_out_channels=(32, 64)					,   layers_per_block=2					,   in_channels=4					,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,   cross_attention_dim=32					,   conditioning_embedding_out_channels=(16, 32)					,   )
    torch.manual_seed(0				)
    a__     				=		DDIMScheduler(
        beta_start=0.0_0085					,   beta_end=0.012					,   beta_schedule='scaled_linear'					,   clip_sample=_snake_case					,   set_alpha_to_one=_snake_case					,   )
    torch.manual_seed(0				)
    a__     				=		AutoencoderKL(
        block_out_channels=[32, 64]					,   in_channels=3					,   out_channels=3					,   down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']					,   up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']					,   latent_channels=4					,   )
    torch.manual_seed(0				)
    a__     				=		CLIPTextConfig(
        bos_token_id=0					,   eos_token_id=2					,   hidden_size=32					,   intermediate_size=37					,   layer_norm_eps=1E-05					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=1000					,   )
    a__     				=		CLIPTextModel(_snake_case				)
    a__     				=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

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


 def        _lowerCAmelCase				(   self		:      List[Any]					,   _snake_case		:      Dict					,   _snake_case		:      Optional[Any]=0				)				->			List[str]:




    '''simple docstring'''






    if str(_snake_case				).startswith('mps'				):
       a__     				=		torch.manual_seed(_snake_case				)
    else:
       a__     				=		torch.Generator(device=_snake_case				).manual_seed(_snake_case				)

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

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

    a__     				=		{
        'prompt': 'A painting of a squirrel eating a burger',
        'generator': generator,
        'num_inference_steps': 2,
        'guidance_scale': 6.0,
        'output_type': 'numpy',
        'image': image,
        'mask_image': mask_image,
        'control_image': control_image,
    }

    return inputs


 def        _lowerCAmelCase				(   self		:      Any				)				->			str:




    '''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        _lowerCAmelCase				(   self		:      List[str]				)				->			List[Any]:




    '''simple docstring'''






    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)



 def        _lowerCAmelCase				(   self		:      Dict				)				->			Optional[Any]:




    '''simple docstring'''






    self._test_inference_batch_single_identical(expected_max_diff=2E-3				)

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 a_       :	List[Any]  			=StableDiffusionControlNetInpaintPipeline
 a_       :	Union[str, Any]  			=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 a_       :	Any  			=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 a_       :	int  			=frozenset([]	)


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




    '''simple docstring'''






    torch.manual_seed(0				)
    a__     				=		UNetaDConditionModel(
        block_out_channels=(32, 64)					,   layers_per_block=2					,   sample_size=32					,   in_channels=4					,   out_channels=4					,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,   cross_attention_dim=32					,   )
    torch.manual_seed(0				)
    a__     				=		ControlNetModel(
        block_out_channels=(32, 64)					,   layers_per_block=2					,   in_channels=4					,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,   cross_attention_dim=32					,   conditioning_embedding_out_channels=(16, 32)					,   )
    torch.manual_seed(0				)
    a__     				=		DDIMScheduler(
        beta_start=0.0_0085					,   beta_end=0.012					,   beta_schedule='scaled_linear'					,   clip_sample=_snake_case					,   set_alpha_to_one=_snake_case					,   )
    torch.manual_seed(0				)
    a__     				=		AutoencoderKL(
        block_out_channels=[32, 64]					,   in_channels=3					,   out_channels=3					,   down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']					,   up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']					,   latent_channels=4					,   )
    torch.manual_seed(0				)
    a__     				=		CLIPTextConfig(
        bos_token_id=0					,   eos_token_id=2					,   hidden_size=32					,   intermediate_size=37					,   layer_norm_eps=1E-05					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=1000					,   )
    a__     				=		CLIPTextModel(_snake_case				)
    a__     				=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

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

class SCREAMING_SNAKE_CASE     (    a   ,      a   ,      unittest.TestCase	):







 """simple docstring"""


 a_       :	List[Any]  			=StableDiffusionControlNetInpaintPipeline
 a_       :	Union[str, Any]  			=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 a_       :	Optional[Any]  			=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS


 def        _lowerCAmelCase				(   self		:      int				)				->			str:




    '''simple docstring'''






    torch.manual_seed(0				)
    a__     				=		UNetaDConditionModel(
        block_out_channels=(32, 64)					,   layers_per_block=2					,   sample_size=32					,   in_channels=9					,   out_channels=4					,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,   cross_attention_dim=32					,   )
    torch.manual_seed(0				)

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

    a__     				=		ControlNetModel(
        block_out_channels=(32, 64)					,   layers_per_block=2					,   in_channels=4					,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,   cross_attention_dim=32					,   conditioning_embedding_out_channels=(16, 32)					,   )
    controlneta.controlnet_down_blocks.apply(_snake_case				)

    torch.manual_seed(0				)
    a__     				=		ControlNetModel(
        block_out_channels=(32, 64)					,   layers_per_block=2					,   in_channels=4					,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,   cross_attention_dim=32					,   conditioning_embedding_out_channels=(16, 32)					,   )
    controlneta.controlnet_down_blocks.apply(_snake_case				)

    torch.manual_seed(0				)
    a__     				=		DDIMScheduler(
        beta_start=0.0_0085					,   beta_end=0.012					,   beta_schedule='scaled_linear'					,   clip_sample=_snake_case					,   set_alpha_to_one=_snake_case					,   )
    torch.manual_seed(0				)
    a__     				=		AutoencoderKL(
        block_out_channels=[32, 64]					,   in_channels=3					,   out_channels=3					,   down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']					,   up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']					,   latent_channels=4					,   )
    torch.manual_seed(0				)
    a__     				=		CLIPTextConfig(
        bos_token_id=0					,   eos_token_id=2					,   hidden_size=32					,   intermediate_size=37					,   layer_norm_eps=1E-05					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=1000					,   )
    a__     				=		CLIPTextModel(_snake_case				)
    a__     				=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

    a__     				=		MultiControlNetModel([controlneta, controlneta]				)

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


 def        _lowerCAmelCase				(   self		:      Optional[Any]					,   _snake_case		:      Optional[Any]					,   _snake_case		:      int=0				)				->			str:




    '''simple docstring'''






    if str(_snake_case				).startswith('mps'				):
       a__     				=		torch.manual_seed(_snake_case				)
    else:
       a__     				=		torch.Generator(device=_snake_case				).manual_seed(_snake_case				)

    a__     				=		2

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

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

    a__     				=		{
        'prompt': 'A painting of a squirrel eating a burger',
        'generator': generator,
        'num_inference_steps': 2,
        'guidance_scale': 6.0,
        'output_type': 'numpy',
        'image': image,
        'mask_image': mask_image,
        'control_image': control_image,
    }

    return inputs


 def        _lowerCAmelCase				(   self		:      List[str]				)				->			Optional[int]:




    '''simple docstring'''






    a__     				=		self.get_dummy_components()
    a__     				=		self.pipeline_class(**_snake_case				)
    pipe.to(_snake_case				)

    a__     				=		10.0
    a__     				=		4

    a__     				=		self.get_dummy_inputs(_snake_case				)
    a__     				=		steps
    a__     				=		scale
    a__     				=		pipe(**_snake_case				)[0]

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

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

    a__     				=		self.get_dummy_inputs(_snake_case				)
    a__     				=		steps
    a__     				=		scale
    a__     				=		pipe(**_snake_case					,   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        _lowerCAmelCase				(   self		:      List[Any]				)				->			Dict:




    '''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        _lowerCAmelCase				(   self		:      Optional[int]				)				->			Any:




    '''simple docstring'''






    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)


 def        _lowerCAmelCase				(   self		:      List[Any]				)				->			Tuple:




    '''simple docstring'''






    self._test_inference_batch_single_identical(expected_max_diff=2E-3				)



 def        _lowerCAmelCase				(   self		:      Optional[int]				)				->			str:




    '''simple docstring'''






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

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







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      str				)				->			int:




    '''simple docstring'''






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


 def        _lowerCAmelCase				(   self		:      int				)				->			Any:




    '''simple docstring'''






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

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

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

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

    a__     				=		'pitch black hole'

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

    a__     				=		pipe(
        _snake_case					,   image=_snake_case					,   mask_image=_snake_case					,   control_image=_snake_case					,   generator=_snake_case					,   output_type='np'					,   num_inference_steps=3					,   )

    a__     				=		output.images[0]

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

    a__     				=		load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'				)

    assert np.abs(expected_image - image				).max() < 9E-2



 def        _lowerCAmelCase				(   self		:      Dict				)				->			Any:




    '''simple docstring'''






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

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

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

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

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

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

    def make_inpaint_condition(_snake_case		:      Any					,   _snake_case		:      int				):
       a__     				=		np.array(image.convert('RGB'				)				).astype(np.floataa				) / 255.0
       a__     				=		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"
       a__     				=		-1.0  # set as masked pixel
       a__     				=		np.expand_dims(_snake_case					,   0				).transpose(0					,   3					,   1					,   2				)
       a__     				=		torch.from_numpy(_snake_case				)
       return image

    a__     				=		make_inpaint_condition(_snake_case					,   _snake_case				)

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

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

    a__     				=		load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'				)

    assert np.abs(expected_image - image				).max() < 9E-2



"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__    				=					{
    "configuration_donut_swin": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutSwinConfig"],
    "processing_donut": ["DonutProcessor"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					[
         "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DonutSwinModel",
         "DonutSwinPreTrainedModel",
     ]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					["DonutFeatureExtractor"]
     __magic_name__    				=					["DonutImageProcessor"]


if TYPE_CHECKING:
     from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
     from .processing_donut import DonutProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_donut_swin import (
              DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              DonutSwinModel,
              DonutSwinPreTrainedModel,
          )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_donut import DonutFeatureExtractor
          from .image_processing_donut import DonutImageProcessor

else:
     import sys

     __magic_name__    				=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


