


# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

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,
    StableDiffusionControlNetImgaImgPipeline,
    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 (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()

class       __magic_name__	(     __lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					,				unittest.TestCase):
     A:		Union[str, Any]			=      StableDiffusionControlNetImgaImgPipeline
     A:		Optional[int]			=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
     A:		Union[str, Any]			=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     A:		List[Any]			=      IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"})
     A:		List[Any]			=      IMAGE_TO_IMAGE_IMAGE_PARAMS


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      Dict:





      '''simple docstring'''






      torch.manual_seed(0			)
      UpperCamelCase__					:    Dict          =   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__					:    Optional[int]          =   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__					:    Optional[Any]          =   DDIMScheduler(
          beta_start=0.0_0085							,	beta_end=0.012							,	beta_schedule='''scaled_linear'''							,	clip_sample=lowerCamelCase__							,	set_alpha_to_one=lowerCamelCase__							,	)
      torch.manual_seed(0			)
      UpperCamelCase__					:    List[str]          =   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__					:    Tuple          =   CLIPTextConfig(
          bos_token_id=0							,	eos_token_id=2							,	hidden_size=32							,	intermediate_size=37							,	layer_norm_eps=1E-0_5							,	num_attention_heads=4							,	num_hidden_layers=5							,	pad_token_id=1							,	vocab_size=1000							,	)
      UpperCamelCase__					:    Any          =   CLIPTextModel(lowerCamelCase__			)
      UpperCamelCase__					:    int          =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

      UpperCamelCase__					:    List[str]          =   {
          '''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							:     Any							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     List[Any]=0			)  ->      List[Any]:





      '''simple docstring'''






      if str(lowerCamelCase__			).startswith('''mps'''			):
       UpperCamelCase__					:    Tuple          =   torch.manual_seed(lowerCamelCase__			)
      else:
       UpperCamelCase__					:    Dict          =   torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   2
      UpperCamelCase__					:    Dict          =   randn_tensor(
          (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)							,	generator=lowerCamelCase__							,	device=torch.device(lowerCamelCase__			)							,	)
      UpperCamelCase__					:    List[Any]          =   floats_tensor(control_image.shape							,	rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   image.cpu().permute(0							,	2							,	3							,	1			)[0]
      UpperCamelCase__					:    List[Any]          =   Image.fromarray(np.uinta(lowerCamelCase__			)			).convert('''RGB'''			).resize((64, 64)			)
      UpperCamelCase__					:    List[Any]          =   {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 6.0,
          '''output_type''': '''numpy''',
          '''image''': image,
          '''control_image''': control_image,
      }

      return inputs


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





      '''simple docstring'''






      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)


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





      '''simple docstring'''






      self._test_inference_batch_single_identical(expected_max_diff=2E-3			)



class       __magic_name__	(     __lowerCAmelCase					,				__lowerCAmelCase					,				unittest.TestCase):
     A:		Dict			=      StableDiffusionControlNetImgaImgPipeline
     A:		Optional[Any]			=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
     A:		int			=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     A:		Union[str, Any]			=      frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess


     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      Dict:





      '''simple docstring'''






      torch.manual_seed(0			)
      UpperCamelCase__					:    Any          =   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			)

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

      UpperCamelCase__					:    Union[str, Any]          =   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(lowerCamelCase__			)

      torch.manual_seed(0			)
      UpperCamelCase__					:    Union[str, Any]          =   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(lowerCamelCase__			)

      torch.manual_seed(0			)
      UpperCamelCase__					:    int          =   DDIMScheduler(
          beta_start=0.0_0085							,	beta_end=0.012							,	beta_schedule='''scaled_linear'''							,	clip_sample=lowerCamelCase__							,	set_alpha_to_one=lowerCamelCase__							,	)
      torch.manual_seed(0			)
      UpperCamelCase__					:    List[Any]          =   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__					:    Union[str, Any]          =   CLIPTextConfig(
          bos_token_id=0							,	eos_token_id=2							,	hidden_size=32							,	intermediate_size=37							,	layer_norm_eps=1E-0_5							,	num_attention_heads=4							,	num_hidden_layers=5							,	pad_token_id=1							,	vocab_size=1000							,	)
      UpperCamelCase__					:    Union[str, Any]          =   CLIPTextModel(lowerCamelCase__			)
      UpperCamelCase__					:    Optional[int]          =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

      UpperCamelCase__					:    List[str]          =   MultiControlNetModel([controlneta, controlneta]			)

      UpperCamelCase__					:    List[str]          =   {
          '''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							:     Dict							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     Tuple=0			)  ->      Dict:





      '''simple docstring'''






      if str(lowerCamelCase__			).startswith('''mps'''			):
       UpperCamelCase__					:    Tuple          =   torch.manual_seed(lowerCamelCase__			)
      else:
       UpperCamelCase__					:    Union[str, Any]          =   torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)

      UpperCamelCase__					:    str          =   2

      UpperCamelCase__					:    Tuple          =   [
          randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)							,	generator=lowerCamelCase__							,	device=torch.device(lowerCamelCase__			)							,	),
          randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)							,	generator=lowerCamelCase__							,	device=torch.device(lowerCamelCase__			)							,	),
      ]

      UpperCamelCase__					:    List[str]          =   floats_tensor(control_image[0].shape							,	rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   image.cpu().permute(0							,	2							,	3							,	1			)[0]
      UpperCamelCase__					:    Optional[int]          =   Image.fromarray(np.uinta(lowerCamelCase__			)			).convert('''RGB'''			).resize((64, 64)			)
      UpperCamelCase__					:    List[Any]          =   {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 6.0,
          '''output_type''': '''numpy''',
          '''image''': image,
          '''control_image''': control_image,
      }

      return inputs


     def      UpperCAmelCase__	(      self							:     int			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   self.get_dummy_components()
      UpperCamelCase__					:    List[Any]          =   self.pipeline_class(**lowerCamelCase__			)
      pipe.to(lowerCamelCase__			)

      UpperCamelCase__					:    Union[str, Any]          =   10.0
      UpperCamelCase__					:    Optional[int]          =   4

      UpperCamelCase__					:    Any          =   self.get_dummy_inputs(lowerCamelCase__			)
      UpperCamelCase__					:    Dict          =   steps
      UpperCamelCase__					:    Optional[Any]          =   scale
      UpperCamelCase__					:    Union[str, Any]          =   pipe(**lowerCamelCase__			)[0]

      UpperCamelCase__					:    List[Any]          =   self.get_dummy_inputs(lowerCamelCase__			)
      UpperCamelCase__					:    List[str]          =   steps
      UpperCamelCase__					:    int          =   scale
      UpperCamelCase__					:    List[Any]          =   pipe(**lowerCamelCase__							,	control_guidance_start=0.1							,	control_guidance_end=0.2			)[0]

      UpperCamelCase__					:    Dict          =   self.get_dummy_inputs(lowerCamelCase__			)
      UpperCamelCase__					:    Dict          =   steps
      UpperCamelCase__					:    Optional[Any]          =   scale
      UpperCamelCase__					:    List[Any]          =   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__					:    Optional[int]          =   steps
      UpperCamelCase__					:    Optional[Any]          =   scale
      UpperCamelCase__					:    Optional[int]          =   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      UpperCAmelCase__	(      self							:     List[str]			)  ->      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      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      List[Any]:





      '''simple docstring'''






      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)


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





      '''simple docstring'''






      self._test_inference_batch_single_identical(expected_max_diff=2E-3			)


     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   self.get_dummy_components()
      UpperCamelCase__					:    int          =   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       __magic_name__	(     unittest.TestCase):


     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      List[Any]:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''			)

      UpperCamelCase__					:    int          =   StableDiffusionControlNetImgaImgPipeline.from_pretrained(
          '''runwayml/stable-diffusion-v1-5'''							,	safety_checker=lowerCamelCase__							,	controlnet=lowerCamelCase__			)
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=lowerCamelCase__			)

      UpperCamelCase__					:    Optional[int]          =   torch.Generator(device='''cpu'''			).manual_seed(0			)
      UpperCamelCase__					:    int          =   '''evil space-punk bird'''
      UpperCamelCase__					:    Optional[int]          =   load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''			).resize((512, 512)			)
      UpperCamelCase__					:    List[Any]          =   load_image(
          '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''			).resize((512, 512)			)

      UpperCamelCase__					:    Tuple          =   pipe(
          lowerCamelCase__							,	lowerCamelCase__							,	control_image=lowerCamelCase__							,	generator=lowerCamelCase__							,	output_type='''np'''							,	num_inference_steps=50							,	strength=0.6							,	)

      UpperCamelCase__					:    Dict          =   output.images[0]

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

      UpperCamelCase__					:    Optional[Any]          =   load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'''			)

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






from typing import TYPE_CHECKING

from ..utils import _LazyModule


__UpperCamelCase       :       int						       =				{
    "config": [
        "EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
        "OnnxConfig",
        "OnnxConfigWithPast",
        "OnnxSeq2SeqConfigWithPast",
        "PatchingSpec",
    ],
    "convert": ["export", "validate_model_outputs"],
    "features": ["FeaturesManager"],
    "utils": ["ParameterFormat", "compute_serialized_parameters_size"],
}


if TYPE_CHECKING:
       from .config import (
           EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
           OnnxConfig,
           OnnxConfigWithPast,
           OnnxSeqaSeqConfigWithPast,
           PatchingSpec,
       )
       from .convert import export, validate_model_outputs
       from .features import FeaturesManager
       from .utils import ParameterFormat, compute_serialized_parameters_size

else:
       import sys

       __UpperCamelCase       :       List[str]						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


