

# 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_       (    __lowercase  ,				__lowercase  ,				__lowercase  ,				unittest.TestCase	):
      UpperCamelCase_     :						Tuple		 =							StableDiffusionControlNetInpaintPipeline
      UpperCamelCase_     :						int		 =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCamelCase_     :						Union[str, Any]		 =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCamelCase_     :						Union[str, Any]		 =							frozenset({"control_image"}	)  # skip `image` and `mask` for now, only test for control_image
      UpperCamelCase_     :						List[Any]		 =							TEXT_TO_IMAGE_IMAGE_PARAMS



      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	str:
        torch.manual_seed(0						)
        _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						)
        _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						)
        _snake_case		=				DDIMScheduler(
            beta_start=0.0_0085     ,   beta_end=0.012     ,   beta_schedule='''scaled_linear'''     ,   clip_sample=A__     ,   set_alpha_to_one=A__     ,   )
        torch.manual_seed(0						)
        _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						)
        _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=1000     ,   )
        _snake_case		=				CLIPTextModel(A__						)
        _snake_case		=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

        _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 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							str     ,   A__ :							Optional[Any]=0						)       ->	Any:
        if str(A__						).startswith('''mps'''						):
          _snake_case		=				torch.manual_seed(A__						)
        else:
          _snake_case		=				torch.Generator(device=A__						).manual_seed(A__						)

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

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

        _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 		UpperCamelCase_						(     self :							str						)       ->	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 		UpperCamelCase_						(     self :							List[Any]						)       ->	Optional[Any]:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3						)







      def 		UpperCamelCase_						(     self :							Optional[Any]						)       ->	Tuple:
        self._test_inference_batch_single_identical(expected_max_diff=2e-3						)



class     lowercase_       (    __lowercase	):
      UpperCamelCase_     :						Union[str, Any]		 =							StableDiffusionControlNetInpaintPipeline
      UpperCamelCase_     :						Any		 =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCamelCase_     :						Optional[int]		 =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCamelCase_     :						Union[str, Any]		 =							frozenset([]	)



      def 		UpperCamelCase_						(     self :							str						)       ->	Optional[int]:
        torch.manual_seed(0						)
        _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						)
        _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						)
        _snake_case		=				DDIMScheduler(
            beta_start=0.0_0085     ,   beta_end=0.012     ,   beta_schedule='''scaled_linear'''     ,   clip_sample=A__     ,   set_alpha_to_one=A__     ,   )
        torch.manual_seed(0						)
        _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						)
        _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=1000     ,   )
        _snake_case		=				CLIPTextModel(A__						)
        _snake_case		=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

        _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     lowercase_       (    __lowercase  ,				__lowercase  ,				unittest.TestCase	):
      UpperCamelCase_     :						Union[str, Any]		 =							StableDiffusionControlNetInpaintPipeline
      UpperCamelCase_     :						Dict		 =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCamelCase_     :						List[str]		 =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS



      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	Optional[Any]:
        torch.manual_seed(0						)
        _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__ :							Dict						):
          if isinstance(A__     ,   torch.nn.Convad						):
            torch.nn.init.normal(m.weight						)
            m.bias.data.fill_(1.0						)

        _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						)
        _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						)
        _snake_case		=				DDIMScheduler(
            beta_start=0.0_0085     ,   beta_end=0.012     ,   beta_schedule='''scaled_linear'''     ,   clip_sample=A__     ,   set_alpha_to_one=A__     ,   )
        torch.manual_seed(0						)
        _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						)
        _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=1000     ,   )
        _snake_case		=				CLIPTextModel(A__						)
        _snake_case		=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

        _snake_case		=				MultiControlNetModel([controlneta, controlneta]						)

        _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 		UpperCamelCase_						(     self :							Optional[int]     ,   A__ :							Any     ,   A__ :							Dict=0						)       ->	Dict:
        if str(A__						).startswith('''mps'''						):
          _snake_case		=				torch.manual_seed(A__						)
        else:
          _snake_case		=				torch.Generator(device=A__						).manual_seed(A__						)

        _snake_case		=				2

        _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__						)     ,   ),
        ]
        _snake_case		=				floats_tensor((1, 3, 32, 32)     ,   rng=random.Random(A__						)						).to(A__						)
        _snake_case		=				init_image.cpu().permute(0     ,   2     ,   3     ,   1						)[0]

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

        _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 		UpperCamelCase_						(     self :							List[str]						)       ->	str:
        _snake_case		=				self.get_dummy_components()
        _snake_case		=				self.pipeline_class(**A__						)
        pipe.to(A__						)

        _snake_case		=				10.0
        _snake_case		=				4

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

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

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

        _snake_case		=				self.get_dummy_inputs(A__						)
        _snake_case		=				steps
        _snake_case		=				scale
        _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 		UpperCamelCase_						(     self :							Tuple						)       ->	int:
        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 		UpperCamelCase_						(     self :							int						)       ->	List[Any]:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3						)



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







      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	Tuple:
        _snake_case		=				self.get_dummy_components()
        _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     lowercase_       (    unittest.TestCase	):



      def 		UpperCamelCase_						(     self :							Dict						)       ->	List[Any]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



      def 		UpperCamelCase_						(     self :							List[str]						)       ->	Union[str, Any]:
        _snake_case		=				ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''						)

        _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__						)

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

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

        _snake_case		=				'''pitch black hole'''

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

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

        _snake_case		=				output.images[0]

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

        _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 		UpperCamelCase_						(     self :							Tuple						)       ->	str:
        _snake_case		=				ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''						)

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

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

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

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

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

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

        _snake_case		=				make_inpaint_condition(A__     ,   A__						)

        _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'''     ,   )
        _snake_case		=				output.images[0]

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

        _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




from typing import TYPE_CHECKING

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


__A     						=      {'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''HubertForCTC''',
     '''HubertForSequenceClassification''',
     '''HubertModel''',
     '''HubertPreTrainedModel''',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFHubertForCTC''',
     '''TFHubertModel''',
     '''TFHubertPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_hubert import (
      HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      HubertForCTC,
      HubertForSequenceClassification,
      HubertModel,
      HubertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_hubert import (
      TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFHubertForCTC,
      TFHubertModel,
      TFHubertPreTrainedModel,
  )


else:
 import sys

 __A     						=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

