
# 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 		__magic_name__				( lowercase_						,lowercase_						,lowercase_						,unittest.TestCase					):


  """simple docstring"""





  _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     _UpperCAmelCase  (					self		):
    torch.manual_seed(0		)
    _lowerCamelCase      =						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		)
    _lowerCamelCase      =						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		)
    _lowerCamelCase      =						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		)
    _lowerCamelCase      =						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		)
    _lowerCamelCase      =						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=10_00	,		)
    _lowerCamelCase      =						CLIPTextModel(a__		)
    _lowerCamelCase      =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

    _lowerCamelCase      =						{
        '''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	,		a__	,		a__=0		):
    if str(a__		).startswith('''mps'''		):
      _lowerCamelCase      =						torch.manual_seed(a__		)
    else:
      _lowerCamelCase      =						torch.Generator(device=a__		).manual_seed(a__		)

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

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

    _lowerCamelCase      =						{
        '''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		):
    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		):
    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)


  def     _UpperCAmelCase  (					self		):
    self._test_inference_batch_single_identical(expected_max_diff=2E-3		)







class 		__magic_name__				( lowercase_					):


  """simple docstring"""





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


  def     _UpperCAmelCase  (					self		):
    torch.manual_seed(0		)
    _lowerCamelCase      =						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		)
    _lowerCamelCase      =						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		)
    _lowerCamelCase      =						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		)
    _lowerCamelCase      =						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		)
    _lowerCamelCase      =						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=10_00	,		)
    _lowerCamelCase      =						CLIPTextModel(a__		)
    _lowerCamelCase      =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

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







class 		__magic_name__				( lowercase_						,lowercase_						,unittest.TestCase					):


  """simple docstring"""





  _UpperCamelCase						 = StableDiffusionControlNetInpaintPipeline
  _UpperCamelCase						 = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  _UpperCamelCase						 = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS


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

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

    _lowerCamelCase      =						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		)
    _lowerCamelCase      =						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		)
    _lowerCamelCase      =						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		)
    _lowerCamelCase      =						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		)
    _lowerCamelCase      =						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=10_00	,		)
    _lowerCamelCase      =						CLIPTextModel(a__		)
    _lowerCamelCase      =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

    _lowerCamelCase      =						MultiControlNetModel([controlneta, controlneta]		)

    _lowerCamelCase      =						{
        '''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	,		a__	,		a__=0		):
    if str(a__		).startswith('''mps'''		):
      _lowerCamelCase      =						torch.manual_seed(a__		)
    else:
      _lowerCamelCase      =						torch.Generator(device=a__		).manual_seed(a__		)

    _lowerCamelCase      =						2

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

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

    _lowerCamelCase      =						{
        '''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		):
    _lowerCamelCase      =						self.get_dummy_components()
    _lowerCamelCase      =						self.pipeline_class(**a__		)
    pipe.to(a__		)

    _lowerCamelCase      =						10.0
    _lowerCamelCase      =						4

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

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

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

    _lowerCamelCase      =						self.get_dummy_inputs(a__		)
    _lowerCamelCase      =						steps
    _lowerCamelCase      =						scale
    _lowerCamelCase      =						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		):
    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		):
    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)


  def     _UpperCAmelCase  (					self		):
    self._test_inference_batch_single_identical(expected_max_diff=2E-3		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.get_dummy_components()
    _lowerCamelCase      =						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 		__magic_name__				( unittest.TestCase					):


  """simple docstring"""





  def     _UpperCAmelCase  (					self		):
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''		)

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

    _lowerCamelCase      =						torch.Generator(device='''cpu'''		).manual_seed(0		)
    _lowerCamelCase      =						load_image(
        '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''		).resize((5_12, 5_12)		)

    _lowerCamelCase      =						load_image(
        '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
        '''/stable_diffusion_inpaint/input_bench_mask.png'''		).resize((5_12, 5_12)		)

    _lowerCamelCase      =						'''pitch black hole'''

    _lowerCamelCase      =						load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''		).resize((5_12, 5_12)		)

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

    _lowerCamelCase      =						output.images[0]

    assert image.shape == (5_12, 5_12, 3)

    _lowerCamelCase      =						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		):
    _lowerCamelCase      =						ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''		)

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

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

    _lowerCamelCase      =						load_image(
        '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'''		)
    _lowerCamelCase      =						init_image.resize((5_12, 5_12)		)

    _lowerCamelCase      =						load_image(
        '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'''		)
    _lowerCamelCase      =						mask_image.resize((5_12, 5_12)		)

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

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

    _lowerCamelCase      =						make_inpaint_condition(a__	,		a__		)

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

    assert image.shape == (5_12, 5_12, 3)

    _lowerCamelCase      =						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






class 		__magic_name__				:


  """simple docstring"""





  class 		__magic_name__				:


    """simple docstring"""





    _UpperCamelCase						 = "_prev", "_data", "_next"


    def __init__(					self	,		a__	,		a__	,		a__		):
      _lowerCamelCase      =						link_p
      _lowerCamelCase      =						element
      _lowerCamelCase      =						link_n


    def     _UpperCAmelCase  (					self		):
      return (
          f''' Prev -> {self._prev is not None}, Next -> {self._next is not None}'''
      )







  def __init__(					self		):
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    _lowerCamelCase      =						self._trailer
    _lowerCamelCase      =						self._header
    _lowerCamelCase      =						0


  def __len__(					self		):
    return self._size


  def     _UpperCAmelCase  (					self		):
    return self.__len__() == 0


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__		):
    # Create new_node by setting it's prev.link -> header
    # setting it's next.link -> trailer
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    _lowerCamelCase      =						new_node
    _lowerCamelCase      =						new_node
    self._size += 1
    return self


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						node._prev
    _lowerCamelCase      =						node._next

    _lowerCamelCase      =						successor
    _lowerCamelCase      =						predecessor
    self._size -= 1
    _lowerCamelCase      =						node._data
    _lowerCamelCase      =						_lowerCamelCase      =						_lowerCamelCase      =						None
    del node
    return temp







class 		__magic_name__				( _DoublyLinkedBase					):


  """simple docstring"""





  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise Exception('''List is empty'''		)
    return self._header._next._data


  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise Exception('''List is empty'''		)
    return self._trailer._prev._data


  def     _UpperCAmelCase  (					self	,		a__		):
    return self._insert(self._header	,		a__	,		self._header._next		)


  def     _UpperCAmelCase  (					self	,		a__		):
    return self._insert(self._trailer._prev	,		a__	,		self._trailer		)


  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise IndexError('''remove_first from empty list'''		)
    return self._delete(self._header._next		)


  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise IndexError('''remove_first from empty list'''		)
    return self._delete(self._trailer._prev		)




