

"""simple docstring"""






import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import BeitImageProcessor






class      _UpperCAmelCase      (		unittest.TestCase):
			def __init__(		self   ,		_A   ,		_A=7   ,		_A=3   ,		_A=18   ,		_A=30   ,		_A=4_00   ,		_A=True   ,		_A=None   ,		_A=True   ,		_A=None   ,		_A=True   ,		_A=[0.5, 0.5, 0.5]   ,		_A=[0.5, 0.5, 0.5]   ,		_A=False   ,		)     ->     List[str]:






				'''simple docstring'''







				_UpperCAmelCase     :				Union[str, Any]          =       size if size is not None else {"height": 20, "width": 20}
				_UpperCAmelCase     :				Union[str, Any]          =       crop_size if crop_size is not None else {"height": 18, "width": 18}
				_UpperCAmelCase     :				Tuple          =       parent
				_UpperCAmelCase     :				List[str]          =       batch_size
				_UpperCAmelCase     :				Any          =       num_channels
				_UpperCAmelCase     :				str          =       image_size
				_UpperCAmelCase     :				int          =       min_resolution
				_UpperCAmelCase     :				int          =       max_resolution
				_UpperCAmelCase     :				Union[str, Any]          =       do_resize
				_UpperCAmelCase     :				Union[str, Any]          =       size
				_UpperCAmelCase     :				Optional[Any]          =       do_center_crop
				_UpperCAmelCase     :				Any          =       crop_size
				_UpperCAmelCase     :				Union[str, Any]          =       do_normalize
				_UpperCAmelCase     :				Dict          =       image_mean
				_UpperCAmelCase     :				str          =       image_std
				_UpperCAmelCase     :				List[Any]          =       do_reduce_labels
			def 				__snake_case			(		self  )     ->     str:






				'''simple docstring'''







				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_center_crop": self.do_center_crop,
				    "crop_size": self.crop_size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_reduce_labels": self.do_reduce_labels,
				}
def    UpperCamelCase       (      )					->      Optional[Any]:
	_UpperCAmelCase     :				str          =       load_dataset("""hf-internal-testing/fixtures_ade20k""",	split="""test"""			)

	_UpperCAmelCase     :				Union[str, Any]          =       Image.open(dataset[0]["""file"""]			)
	_UpperCAmelCase     :				List[str]          =       Image.open(dataset[1]["""file"""]			)

	return image, map
def    UpperCamelCase       (      )					->      Tuple:
	_UpperCAmelCase     :				Union[str, Any]          =       load_dataset("""hf-internal-testing/fixtures_ade20k""",	split="""test"""			)

	_UpperCAmelCase     :				Optional[int]          =       Image.open(ds[0]["""file"""]			)
	_UpperCAmelCase     :				Union[str, Any]          =       Image.open(ds[1]["""file"""]			)
	_UpperCAmelCase     :				Tuple          =       Image.open(ds[2]["""file"""]			)
	_UpperCAmelCase     :				List[str]          =       Image.open(ds[3]["""file"""]			)

	return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class      _UpperCAmelCase      (		__a     ,  unittest.TestCase):
			__a			:							Optional[int]					       =				BeitImageProcessor if is_vision_available() else None
			def 				__snake_case			(		self  )     ->     str:






				'''simple docstring'''







				_UpperCAmelCase     :				List[str]          =       BeitImageProcessingTester(self  )
			@property
			def 				__snake_case			(		self  )     ->     Optional[Any]:






				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()
			def 				__snake_case			(		self  )     ->     int:






				'''simple docstring'''







				_UpperCAmelCase     :				int          =       self.image_processing_class(**self.image_processor_dict  )
				self.assertTrue(hasattr(_A   ,		"""do_resize"""  )  )
				self.assertTrue(hasattr(_A   ,		"""size"""  )  )
				self.assertTrue(hasattr(_A   ,		"""do_center_crop"""  )  )
				self.assertTrue(hasattr(_A   ,		"""center_crop"""  )  )
				self.assertTrue(hasattr(_A   ,		"""do_normalize"""  )  )
				self.assertTrue(hasattr(_A   ,		"""image_mean"""  )  )
				self.assertTrue(hasattr(_A   ,		"""image_std"""  )  )
			def 				__snake_case			(		self  )     ->     List[Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				Optional[Any]          =       self.image_processing_class.from_dict(self.image_processor_dict  )
				self.assertEqual(image_processor.size   ,		{"""height""": 20, """width""": 20}  )
				self.assertEqual(image_processor.crop_size   ,		{"""height""": 18, """width""": 18}  )
				self.assertEqual(image_processor.do_reduce_labels   ,		_A  )

				_UpperCAmelCase     :				Optional[Any]          =       self.image_processing_class.from_dict(
				    self.image_processor_dict   ,		size=42   ,		crop_size=84   ,		reduce_labels=_A  )
				self.assertEqual(image_processor.size   ,		{"""height""": 42, """width""": 42}  )
				self.assertEqual(image_processor.crop_size   ,		{"""height""": 84, """width""": 84}  )
				self.assertEqual(image_processor.do_reduce_labels   ,		_A  )
			def 				__snake_case			(		self  )     ->     Dict:






				'''simple docstring'''







				pass
			def 				__snake_case			(		self  )     ->     str:






				'''simple docstring'''







				_UpperCAmelCase     :				str          =       self.image_processing_class(**self.image_processor_dict  )
				# create random PIL images
				_UpperCAmelCase     :				str          =       prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_A  )
				for image in image_inputs:
					self.assertIsInstance(_A   ,		Image.Image  )

				# Test not batched input
				_UpperCAmelCase     :				Dict          =       image_processing(image_inputs[0]   ,		return_tensors="""pt"""  ).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,		(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["""height"""],
				        self.image_processor_tester.crop_size["""width"""],
				    )   ,		)

				# Test batched
				_UpperCAmelCase     :				str          =       image_processing(_A   ,		return_tensors="""pt"""  ).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,		(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["""height"""],
				        self.image_processor_tester.crop_size["""width"""],
				    )   ,		)
			def 				__snake_case			(		self  )     ->     List[Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				int          =       self.image_processing_class(**self.image_processor_dict  )
				# create random numpy tensors
				_UpperCAmelCase     :				Union[str, Any]          =       prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_A   ,		numpify=_A  )
				for image in image_inputs:
					self.assertIsInstance(_A   ,		np.ndarray  )

				# Test not batched input
				_UpperCAmelCase     :				Optional[int]          =       image_processing(image_inputs[0]   ,		return_tensors="""pt"""  ).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,		(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["""height"""],
				        self.image_processor_tester.crop_size["""width"""],
				    )   ,		)

				# Test batched
				_UpperCAmelCase     :				Union[str, Any]          =       image_processing(_A   ,		return_tensors="""pt"""  ).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,		(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["""height"""],
				        self.image_processor_tester.crop_size["""width"""],
				    )   ,		)
			def 				__snake_case			(		self  )     ->     List[str]:






				'''simple docstring'''







				_UpperCAmelCase     :				Any          =       self.image_processing_class(**self.image_processor_dict  )
				# create random PyTorch tensors
				_UpperCAmelCase     :				Tuple          =       prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_A   ,		torchify=_A  )
				for image in image_inputs:
					self.assertIsInstance(_A   ,		torch.Tensor  )

				# Test not batched input
				_UpperCAmelCase     :				List[str]          =       image_processing(image_inputs[0]   ,		return_tensors="""pt"""  ).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,		(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["""height"""],
				        self.image_processor_tester.crop_size["""width"""],
				    )   ,		)

				# Test batched
				_UpperCAmelCase     :				Tuple          =       image_processing(_A   ,		return_tensors="""pt"""  ).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,		(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["""height"""],
				        self.image_processor_tester.crop_size["""width"""],
				    )   ,		)
			def 				__snake_case			(		self  )     ->     str:






				'''simple docstring'''







				_UpperCAmelCase     :				Tuple          =       self.image_processing_class(**self.image_processor_dict  )
				# create random PyTorch tensors
				_UpperCAmelCase     :				str          =       prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_A   ,		torchify=_A  )
				_UpperCAmelCase     :				Tuple          =       []
				for image in image_inputs:
					self.assertIsInstance(_A   ,		torch.Tensor  )
					maps.append(torch.zeros(image.shape[-2:]  ).long()  )

				# Test not batched input
				_UpperCAmelCase     :				Union[str, Any]          =       image_processing(image_inputs[0]   ,		maps[0]   ,		return_tensors="""pt"""  )
				self.assertEqual(
				    encoding["""pixel_values"""].shape   ,		(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["""height"""],
				        self.image_processor_tester.crop_size["""width"""],
				    )   ,		)
				self.assertEqual(
				    encoding["""labels"""].shape   ,		(
				        1,
				        self.image_processor_tester.crop_size["""height"""],
				        self.image_processor_tester.crop_size["""width"""],
				    )   ,		)
				self.assertEqual(encoding["""labels"""].dtype   ,		torch.long  )
				self.assertTrue(encoding["""labels"""].min().item() >= 0  )
				self.assertTrue(encoding["""labels"""].max().item() <= 2_55  )

				# Test batched
				_UpperCAmelCase     :				str          =       image_processing(_A   ,		_A   ,		return_tensors="""pt"""  )
				self.assertEqual(
				    encoding["""pixel_values"""].shape   ,		(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["""height"""],
				        self.image_processor_tester.crop_size["""width"""],
				    )   ,		)
				self.assertEqual(
				    encoding["""labels"""].shape   ,		(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.crop_size["""height"""],
				        self.image_processor_tester.crop_size["""width"""],
				    )   ,		)
				self.assertEqual(encoding["""labels"""].dtype   ,		torch.long  )
				self.assertTrue(encoding["""labels"""].min().item() >= 0  )
				self.assertTrue(encoding["""labels"""].max().item() <= 2_55  )

				# Test not batched input (PIL images)
				_UpperCAmelCase     :				Dict          =       prepare_semantic_single_inputs()

				_UpperCAmelCase     :				Tuple          =       image_processing(_A   ,		_A   ,		return_tensors="""pt"""  )
				self.assertEqual(
				    encoding["""pixel_values"""].shape   ,		(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["""height"""],
				        self.image_processor_tester.crop_size["""width"""],
				    )   ,		)
				self.assertEqual(
				    encoding["""labels"""].shape   ,		(
				        1,
				        self.image_processor_tester.crop_size["""height"""],
				        self.image_processor_tester.crop_size["""width"""],
				    )   ,		)
				self.assertEqual(encoding["""labels"""].dtype   ,		torch.long  )
				self.assertTrue(encoding["""labels"""].min().item() >= 0  )
				self.assertTrue(encoding["""labels"""].max().item() <= 2_55  )

				# Test batched input (PIL images)
				_UpperCAmelCase     :				int          =       prepare_semantic_batch_inputs()

				_UpperCAmelCase     :				Tuple          =       image_processing(_A   ,		_A   ,		return_tensors="""pt"""  )
				self.assertEqual(
				    encoding["""pixel_values"""].shape   ,		(
				        2,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["""height"""],
				        self.image_processor_tester.crop_size["""width"""],
				    )   ,		)
				self.assertEqual(
				    encoding["""labels"""].shape   ,		(
				        2,
				        self.image_processor_tester.crop_size["""height"""],
				        self.image_processor_tester.crop_size["""width"""],
				    )   ,		)
				self.assertEqual(encoding["""labels"""].dtype   ,		torch.long  )
				self.assertTrue(encoding["""labels"""].min().item() >= 0  )
				self.assertTrue(encoding["""labels"""].max().item() <= 2_55  )
			def 				__snake_case			(		self  )     ->     Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase     :				int          =       self.image_processing_class(**self.image_processor_dict  )

				# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
				_UpperCAmelCase     :				str          =       prepare_semantic_single_inputs()
				_UpperCAmelCase     :				int          =       image_processing(_A   ,		_A   ,		return_tensors="""pt"""  )
				self.assertTrue(encoding["""labels"""].min().item() >= 0  )
				self.assertTrue(encoding["""labels"""].max().item() <= 1_50  )

				_UpperCAmelCase     :				int          =       True
				_UpperCAmelCase     :				List[str]          =       image_processing(_A   ,		_A   ,		return_tensors="""pt"""  )
				self.assertTrue(encoding["""labels"""].min().item() >= 0  )
				self.assertTrue(encoding["""labels"""].max().item() <= 2_55  )













'''simple docstring'''
# 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 							_UpperCAmelCase    (		snake_case					,       snake_case					,       snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: Dict									=     StableDiffusionControlNetImgaImgPipeline
    __lowerCamelCase: Union[str, Any]									=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
    __lowerCamelCase: Optional[Any]									=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowerCamelCase: Any									=     IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}      )
    __lowerCamelCase: Dict									=     IMAGE_TO_IMAGE_IMAGE_PARAMS




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       torch.manual_seed(0      )
       lowercase_						:     Tuple							     =  UNetaDConditionModel(
           block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  sample_size=3_2	,  in_channels=4	,  out_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,  cross_attention_dim=3_2	,  )
       torch.manual_seed(0      )
       lowercase_						:     Optional[Any]							     =  ControlNetModel(
           block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  in_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  cross_attention_dim=3_2	,  conditioning_embedding_out_channels=(1_6, 3_2)	,  )
       torch.manual_seed(0      )
       lowercase_						:     List[Any]							     =  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      )
       lowercase_						:     Union[str, Any]							     =  AutoencoderKL(
           block_out_channels=[3_2, 6_4]	,  in_channels=3	,  out_channels=3	,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,  latent_channels=4	,  )
       torch.manual_seed(0      )
       lowercase_						:     str							     =  CLIPTextConfig(
           bos_token_id=0	,  eos_token_id=2	,  hidden_size=3_2	,  intermediate_size=3_7	,  layer_norm_eps=1e-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=1_0_0_0	,  )
       lowercase_						:     List[str]							     =  CLIPTextModel(a      )
       lowercase_						:     Any							     =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

       lowercase_						:     Tuple							     =  {
           "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[str]	,  a	:							List[str]	,  a	:							Any=0      ):






       '''simple docstring'''






       if str(a      ).startswith("mps"      ):
          lowercase_						:     int							     =  torch.manual_seed(a      )
       else:
          lowercase_						:     Optional[int]							     =  torch.Generator(device=a      ).manual_seed(a      )

       lowercase_						:     Optional[Any]							     =  2
       lowercase_						:     List[str]							     =  randn_tensor(
           (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)	,  generator=a	,  device=torch.device(a      )	,  )
       lowercase_						:     List[Any]							     =  floats_tensor(control_image.shape	,  rng=random.Random(a      )      ).to(a      )
       lowercase_						:     List[str]							     =  image.cpu().permute(0	,  2	,  3	,  1      )[0]
       lowercase_						:     Tuple							     =  Image.fromarray(np.uinta(a      )      ).convert("RGB"      ).resize((6_4, 6_4)      )
       lowercase_						:     List[str]							     =  {
           "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 					lowerCAmelCase__					(      self	:							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	:							Optional[Any]      ):






       '''simple docstring'''






       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3      )





    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       self._test_inference_batch_single_identical(expected_max_diff=2e-3      )


class 							_UpperCAmelCase    (		snake_case					,       snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: Optional[Any]									=     StableDiffusionControlNetImgaImgPipeline
    __lowerCamelCase: List[Any]									=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
    __lowerCamelCase: Optional[int]									=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowerCamelCase: Dict									=     frozenset([]      )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       torch.manual_seed(0      )
       lowercase_						:     Union[str, Any]							     =  UNetaDConditionModel(
           block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  sample_size=3_2	,  in_channels=4	,  out_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,  cross_attention_dim=3_2	,  )
       torch.manual_seed(0      )

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

       lowercase_						:     Optional[int]							     =  ControlNetModel(
           block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  in_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  cross_attention_dim=3_2	,  conditioning_embedding_out_channels=(1_6, 3_2)	,  )
       controlneta.controlnet_down_blocks.apply(a      )

       torch.manual_seed(0      )
       lowercase_						:     Tuple							     =  ControlNetModel(
           block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  in_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  cross_attention_dim=3_2	,  conditioning_embedding_out_channels=(1_6, 3_2)	,  )
       controlneta.controlnet_down_blocks.apply(a      )

       torch.manual_seed(0      )
       lowercase_						:     Optional[int]							     =  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      )
       lowercase_						:     int							     =  AutoencoderKL(
           block_out_channels=[3_2, 6_4]	,  in_channels=3	,  out_channels=3	,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,  latent_channels=4	,  )
       torch.manual_seed(0      )
       lowercase_						:     List[str]							     =  CLIPTextConfig(
           bos_token_id=0	,  eos_token_id=2	,  hidden_size=3_2	,  intermediate_size=3_7	,  layer_norm_eps=1e-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=1_0_0_0	,  )
       lowercase_						:     Optional[int]							     =  CLIPTextModel(a      )
       lowercase_						:     Tuple							     =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

       lowercase_						:     Union[str, Any]							     =  MultiControlNetModel([controlneta, controlneta]      )

       lowercase_						:     Dict							     =  {
           "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]	,  a	:							List[str]	,  a	:							Optional[Any]=0      ):






       '''simple docstring'''






       if str(a      ).startswith("mps"      ):
          lowercase_						:     str							     =  torch.manual_seed(a      )
       else:
          lowercase_						:     Any							     =  torch.Generator(device=a      ).manual_seed(a      )

       lowercase_						:     Dict							     =  2

       lowercase_						:     Union[str, Any]							     =  [
           randn_tensor(
               (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)	,  generator=a	,  device=torch.device(a      )	,  ),
           randn_tensor(
               (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)	,  generator=a	,  device=torch.device(a      )	,  ),
       ]

       lowercase_						:     Dict							     =  floats_tensor(control_image[0].shape	,  rng=random.Random(a      )      ).to(a      )
       lowercase_						:     Tuple							     =  image.cpu().permute(0	,  2	,  3	,  1      )[0]
       lowercase_						:     Optional[int]							     =  Image.fromarray(np.uinta(a      )      ).convert("RGB"      ).resize((6_4, 6_4)      )
       lowercase_						:     Optional[int]							     =  {
           "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 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  self.get_dummy_components()
       lowercase_						:     Any							     =  self.pipeline_class(**a      )
       pipe.to(a      )

       lowercase_						:     str							     =  10.0
       lowercase_						:     Dict							     =  4

       lowercase_						:     Optional[Any]							     =  self.get_dummy_inputs(a      )
       lowercase_						:     str							     =  steps
       lowercase_						:     List[str]							     =  scale
       lowercase_						:     Dict							     =  pipe(**a      )[0]

       lowercase_						:     Optional[Any]							     =  self.get_dummy_inputs(a      )
       lowercase_						:     Dict							     =  steps
       lowercase_						:     Optional[int]							     =  scale
       lowercase_						:     Optional[int]							     =  pipe(**a	,  control_guidance_start=0.1	,  control_guidance_end=0.2      )[0]

       lowercase_						:     List[str]							     =  self.get_dummy_inputs(a      )
       lowercase_						:     Any							     =  steps
       lowercase_						:     Optional[int]							     =  scale
       lowercase_						:     str							     =  pipe(**a	,  control_guidance_start=[0.1, 0.3]	,  control_guidance_end=[0.2, 0.7]      )[0]

       lowercase_						:     Union[str, Any]							     =  self.get_dummy_inputs(a      )
       lowercase_						:     Union[str, Any]							     =  steps
       lowercase_						:     Optional[Any]							     =  scale
       lowercase_						:     Dict							     =  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 					lowerCAmelCase__					(      self	:							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 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3      )




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       self._test_inference_batch_single_identical(expected_max_diff=2e-3      )





    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  self.get_dummy_components()
       lowercase_						:     List[str]							     =  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 							_UpperCAmelCase    (		unittest.TestCase      ):




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






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





    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"      )

       lowercase_						:     int							     =  StableDiffusionControlNetImgaImgPipeline.from_pretrained(
           "runwayml/stable-diffusion-v1-5"	,  safety_checker=a	,  controlnet=a      )
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     Optional[int]							     =  torch.Generator(device="cpu"      ).manual_seed(0      )
       lowercase_						:     List[str]							     =  "evil space-punk bird"
       lowercase_						:     Union[str, Any]							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"      ).resize((5_1_2, 5_1_2)      )
       lowercase_						:     List[str]							     =  load_image(
           "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"      ).resize((5_1_2, 5_1_2)      )

       lowercase_						:     int							     =  pipe(
           a	,  a	,  control_image=a	,  generator=a	,  output_type="np"	,  num_inference_steps=5_0	,  strength=0.6	,  )

       lowercase_						:     Any							     =  output.images[0]

       assert image.shape == (5_1_2, 5_1_2, 3)

       lowercase_						:     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




